Underwater Moving Target Classification Using Multilayer

applied sciences

Article Underwater Moving Target Classiﬁcation Using Multilayer Processing of Active Sonar System

Iksu Seo 1, Seongweon Kim 1, Youngwoo Ryu 1, Jungyong Park 1 and Dong Seog Han 2,*

1 Agency for Defense Development, Jinhae 51678, Korea; [email protected] (I.S.); [email protected] (S.K.); [email protected] (Y.R.); [email protected] (J.P.) 2 School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea * Correspondence: [email protected]; Tel.: +82-53-950-6609

Received: 27 September 2019; Accepted: 26 October 2019; Published: 30 October 2019

Featured Application: The multilayer classiﬁcation algorithm proposed in this paper can be applied to active sonar systems in order to detect long-range targets.

Abstract: The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return’s SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm’s classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.

Keywords: active sonar; target classiﬁcation; support vector machine; underwater target detection

1. Introduction In active sonar systems, clutter degrades the performance of target detection and overwhelms sonar operators conducting antisubmarine warfare (ASW). Clutter consists of reﬂected signals from underwater surfaces, the bottom, rocks, ﬁsh, and other ships not of interest to sonar operators. Traditionally, underwater target detection depends on the decisions of well-trained sonar operators. This method can be highly inaccurate due to the need for continuous monitoring of the operating sonar

Appl. Sci. 2019, 9, 4617; doi:10.3390/app9214617 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 4617 2 of 15 console. Moreover, varying underwater environments and target moving patterns make continuous detection and classification difficult, highlighting the need for effective detection and classification algorithms in such an environment. Underwater moving target detection is challenging for the following reasons:

1. Complex underwater environment: Underwater target detection is a complex pattern classification problem involving time-varying and mixed environments. Environmental noise causes acoustic signal waveform distortion, loss of signal information, and incomplete received acoustic signals due to the complexity of the sound propagation medium. 2. Unknown target orientation: Normally the target’s orientation is unknown. Aspect-dependent target strength results in a variable signal-to-noise ratio (SNR) and causes the loss of feature information. 3. Continuous target tracking: Continuous classification and tracking of weak target echoes should be desirable because a target can be a threat and can take action upon recognizing it has been detected. 4. Less feature information: Active sonar for long-range target detection has low-resolution data, so a feature vector extraction algorithm is necessary. 5. Long interval ping-to-ping: In order to detect long-range targets, a long pulse repetition interval (PRI) is used; this means that a relatively small amount of data is acquired over time. 6. Lack of data in many cases: It is difficult to acquire sufficient and representative sea experiment data.

Many algorithms have been applied to active sonar detection and classification. Detection using a Markov random field [1], a contrast box detector based on statistical features of reverberation, and morphology detection of an object [2,3] were studied for the separation of targets from reverberation. The morphology detector distinguishes the characteristics of the target and reverberation signal and processes them under the condition that the target signal is in an isolated area and the reverberation signal has a distribution of several clutters. These techniques are effective for reverberation removal but have the disadvantage of lowering the target detection rate in a single ping. Multiple ping imaging techniques using temporal and spatial features of clutter and the target have been proposed [4,5]. The cumulative processing of multiple ping data reduces the classification rate in a single ping. It also has the disadvantage of using multiple ping signals because signals that are not filtered out of a single ping are used in multiple pings. There are also many approaches to mine detection using side-scan sonar [6–8] with high-resolution data. Mine detection approaches are for a near field detection environment that processes high-resolution image data. Therefore, it is difficult to apply in active sonar for long-range target detection with low-resolution data. Much research has been carried out since a mine’s sonar signature exhibits variable characteristics according to its position and the ping’s angle of incidence at the seafloor. Seo et al. [9] studied target separation from clutter using spectral feature information on the seabed floor. In [9], the mathematical model of a cylindrical object proposed by Ye [10] was used to generate target signals, and clutter signals based on the K-distribution model introduced by Abraham and Lyons [11,12] were generated. A logistic regression model with discrete Fourier transform (DFT) features trained with the simulated data was applied to experimental data to evaluate classification performance. It is difficult to acquire experiment data for training in underwater environments. Therefore, the results show that this approach could be an alternative method due to a lack of experiment data. In other target echo analyses, many statistical models representing target backscattering have been studied, such as Weibull; log-normal (LN) models with varying levels of physical basis, such as the Rician distribution; and Swerling models and their extensions [13–15]. A scheme based on a time-reversal technique was proposed to improve the detection of a cylindrical object placed proud on the seafloor [16]. However, there is a lack of research on long-range target detection that considers the aspect angle of a moving target and the variety of underwater environments. Appl. Sci. 2019, 9, 4617 3 of 15

The underwater noise level is a function of the environment’s elements, such as the sea state and surrounding sea traffic; this ultimately affects the intensity and patterns of echoes. The target’s echo strength mainly depends on the type of target and the aspect angle of the incident-transmitted signal. These factors affect the SNR of the target echo and echo patterns. They eventually lead to variable SNR in a sonar’s signal processing and make it more difficult to continuously detect and classify targets. In particular, low SNR signals do not yield enough feature information to distinguish them from clutter. For this reason, much research has studied returns above a preset threshold to solve the classification problem. These signals in the matched filter output data are selected for tracking, classification, and console display by sonar operators. However, the extraction process of these signals is inaccurate since the operators empirically adjust the threshold. If target echo SNR is under the threshold by the operators, the target cannot be detected and classified. Thus, extracted data from the matched filter output are very important because the data affect the operator’s manual tracking and classification performance. In most military applications, it is important to continuously track and classify the target. The long PRI required to detect long-range targets means that less data can be obtained over time, so it is hard to know the changes in environmental noise and target strength soon enough to take action. For this reason, we propose a multilayer classification algorithm, which is applicable to all signals without presetting the threshold in one ping’s data regardless of the signal SNR. In this paper, the approach to obtaining feature information incorporates two factors. One is that since a target submarine has a typical type, size, material and volume, the distributed energy pattern of its echo can be different than that from clutter. Second, the sources affecting the SNR of echoes are the ambient noise and target strength according to the ping’s incident angle. These sources are reflected in the signal processing output and can be extracted as feature vectors from different echo areas. In general, the target return strength for a ping abeam (long side) of a submarine target is 15 dB while a head-on ping off the front is known to be 5 dB [17]. This means that the SNR of the target signal varies over at least a 10-dB difference according to the aspect angle of a submarine, and thus may lead to classification performance degradation. Even if the energy in the edge region of the target echo is reduced by noise or decreased by the target aspect, the feature information of the remaining echo energy region near the center point can be retained. We focus on the surviving classifiable feature components from variations in target echo strength by extracting the spatial feature information in the sonar’s matched filter output. Much research has concentrated on the separation of feature vectors by labeling target signals and clutter signals of sufficient intensity. There have also been efforts to separate ambient reverberations. In this paper, we consider a real target as prior target information and assume that it has characteristic features governing the echo appearance of the matched filter output (range-bearing output) using statistical parameters. The performance of the proposed algorithm is evaluated in terms of classification [18,19] and the false alarm rate. A support vector machine (SVM) is utilized to separate the feature vectors. The rest of this paper is organized as follows: In Section2, the SVM is introduced. Section3 describes the multilayer classification algorithm. Section4 shows the experiment, results, and discussions. Finally, in Section5, we summarize our conclusions.

2. Introduction to the Support Vector Machine The SVM enables linear separation by transforming an input space with nonlinear characteristics into a feature space of more dimensions by using a kernel function that can provide an optimal solution. It has attracted a great deal of attention due to the novelty of the concepts that it brings to pattern recognition, its strong mathematical foundation, and its excellent results in practical problems [20,21]. We assume that data samples are as follows:

S = (X , d )N , d = 1, 1., (1) i i i=1 i − where X and N are input samples and the number of samples, respectively. Appl. Sci. 2019, 9, 4617 4 of 15

Appl. Sci. 2019, 9, x FOR PEER REVIEW 4 of 15 We find a hyper-plane y = W X + b with the smallest norm of the coefficient W 2 having · 2 k k the largest margin (2/ W ), where W and b are weight vectorΦ(W) = and1 W bias, respectively. To find optimal hyper-plane, kwek use the minimized function 2 under the constraint an optimal hyper-plane, we use the minimized function Φ(W) = 1 W 2 under the constraint d []()X ⋅W +b ≥1, i =1,2,...,N 2 k k d [(iX iW) + b] 1, i = 1, 2, ..., ,whereN, where the theoperation operation is an is aninner inner product. product. The The solution solution to to this this i i · ≥ optimizationoptimization is is given given byby thethe saddlesaddle pointpoint ofof thethe LagrangianLagrangian as: N 1 2 N T  L(W,b,α) = 1 W −Xα n{y [W ⋅X +b]−1}., (2) L(W, b, α) = 2 W 2 α i y [iWT X +i b ] 1., (2) 2k k − i=1 i i · i −  i=1  α where i is the Lagrange multiplier. where αi is the Lagrange multiplier. AA multi-layer multi-layer perceptronperceptron neuralneural networknetwork (MLP NN) and support vector vector machine machine (SVM) (SVM) were were usedused to to analyze analyze thethe classificationclassification performanceperformance and SVM showed showed better better results results for for sonar sonar signals signals [20]. [20]. In [20], MLP NN had been shown to be easily affected by learning algorithms, input samples, and In [20], MLP NN had been shown to be easily affected by learning algorithms, input samples, and initial variables. In contrast, it showed that SVM has a relatively robust classification capability. The initial variables. In contrast, it showed that SVM has a relatively robust classification capability. performance analysis of four classifier algorithms, multivariate Gaussian, evidential K-nearest The performance analysis of four classifier algorithms, multivariate Gaussian, evidential K-nearest neighbor (K-NN), probabilistic neural network (PNN), and SVM, were conducted from data for near- neighbor (K-NN), probabilistic neural network (PNN), and SVM, were conducted from data for field detection [22]. The result showed that the SVM had better performance for high dimensional near-field detection [22]. The result showed that the SVM had better performance for high dimensional features and the kernel function was an important factor. In [22], the results showed that SVM was features and the kernel function was an important factor. In [22], the results showed that SVM was more more robust for an unbalanced number of training samples, that is, the number of the target sample robust for an unbalanced number of training samples, that is, the number of the target sample is small is small and the clutter is huge. In addition, SVM was less affected by the scattered and mixed and the clutter is huge. In addition, SVM was less affected by the scattered and mixed characteristics characteristics of the underwater environment. A study on sonar data classification according to the of the underwater environment. A study on sonar data classification according to the kernel function kernel function of SVM [23] was performed. In [23], the results showed that the radial basis function of SVM [23] was performed. In [23], the results showed that the radial basis function (RBF) kernel had (RBF) kernel had better performance than polynomial kernel. For these reasons, we use a SVM better performance than polynomial kernel. For these reasons, we use a SVM classifier and a Gaussian classifier and a Gaussian function type RBF kernel, which has already been widely used for sonar functionclassification. type RBF kernel, which has already been widely used for sonar classification. 3. Multilayer Detection Algorithm (Feature Extraction) 3. Multilayer Detection Algorithm (Feature Extraction) In this paper, we extract feature vectors by segmenting layers in the matched filter output of the In this paper, we extract feature vectors by segmenting layers in the matched filter output of the sonar system. The statistical parameters in each layer and between layers are used as feature vectors. sonar system. The statistical parameters in each layer and between layers are used as feature vectors. Figure1 shows the block diagram of the proposed multilayer processing scheme. The multilayer Figure 1 shows the block diagram of the proposed multilayer processing scheme. The multilayer classification algorithm is performed as a classification-before-detection concept with the matched filter classification algorithm is performed as a classification-before-detection concept with the matched output (bearing and range). The results of multilayer classification are used as tracking measurements filter output (bearing and range). The results of multilayer classification are used as tracking andmeasurements are displayed and on are the displayed sonar console on the for sonar each consol ping.e Thesefor each results ping. areThese very results important are very for important automatic trackingfor automatic and detection tracking byand the detection sonar operator. by the sonar operator.

FigureFigure 1.1. The proposed multilayer classification classiﬁcation diagram. diagram.

3.1. Beamforming and Signal Processing Appl. Sci. 2019, 9, 4617 5 of 15

Appl. Sci. 2019, 9, x FOR PEER REVIEW 5 of 15 3.1. Beamforming and Signal Processing Sensor data from cylindrical hull-mounted arrays were beamformed using a Hanning window and matchSensor filtered. data from Delay-and-sum cylindrical hull-mountedbeamforming arraysand polyphase were beamformed filtration with using an a interpolation Hanning window rate ofand 32 matchwere performed filtered. Delay-and-sum in the time domain. beamforming In order and to polyphase reduce the filtration effect of with ship an movement interpolation in the rate beamformingof 32 were performed stage, compensation in the time for domain. sensor position In order change to reduce was theperformed. effect of The ship spatial movement position in theof thebeamforming sensor array stage, was compensation recalculated forfrom sensor ownshi positionp pitch change and roll was data. performed. Then, Thebeamforming spatial position was performedof the sensor with array the new was position recalculated of the fromsensor ownship as beam pitchstabilizatio and rolln. The data. sound Then, speed beamforming value was also was usedperformed for beamforming with the new in real position time. of the sensor as beam stabilization. The sound speed value was also usedIn for the beamforming signal processing in real stage, time. relative Doppler compensation, pulse replica correlation, max DopplerIn thepicking, signal and processing two-dimensional stage, relative normalization Doppler compensation, were performed. pulse In replica order correlation,to remove maxthe transmittingDoppler picking, and receiving and two-dimensional Doppler effect, normalization ownship’s Doppler were performed. nullification In inorder the received to remove beam the outputtransmitting was processed and receiving with the Doppler course eff andect, speed ownship’s data Dopplerof ownship nullification by considering in the the received maximum beam speed output ofwas the processedtarget candidate. with the The course correlation and speed of the datatransmitted of ownship pulse by replica considering was performed the maximum for continuous speed of wavethe target (CW) candidate. and linearly The frequency-modulated correlation of the transmitted (LFM) signals. pulse The replica CW was signal performed is a continuous for continuous wave signal,wave (CW)with the and pulse linearly length frequency-modulated based on the center (LFM) frequency. signals. The The LFM CW signal ishas a continuousa bandwidth wave of severalsignal, hundred with the Hz pulse with length respect based to the on thecenter center frequency, frequency. and Thelinearly LFM modulates signal has it a bandwidthby the pulse of length.several The hundred CW signal Hz with is useful respect for to deriving the center Doppl frequency,er information and linearly from modulates a target itand by the the LFM pulse signal length. hasThe good CW signalrange isresolution. useful for derivingFrom Doppler Doppler data information in 1-kt steps, from abearing target andand the range LFM data signal with has max good Dopplerrange resolution. were extracted. From DopplerThen, the data split in two 1-kt pass steps, mean bearing (S2PM) and rangealgorithm data [24], with which max Doppler has a good were performanceextracted. Then, with thesimple split calculations, two pass mean was (S2PM) used for algorithm noise normalization [24], which hasin two a good dimensions. performance The gap with andsimple window calculations, size of wasS2PM used were for selected noise normalization by considering in two the dimensions. ambiguity function The gap andof the window transmitted size of pulse,S2PM the were echo selected energy by areas, considering and the theresolution ambiguity of the function bearing of and the range transmitted in the matched pulse, the filter echo output. energy Echoareas, energy and the area resolution was considered of the bearing to be the and area range from in thethe matchedpeak to the filter local output. minimum. Echo energyFor each area ping, was theconsidered beam and to range be the for area the from peak the echo peak were to theacquired local minimum.by using target For each position ping, data. the beamOnly andone rangeecho datumfor the for peak each echo ping were was acquired acquired byas usingthe target target beca positionuse, in data.this experiment, Only one echo there datum is only for one each target. ping Thewas other acquired echoes, as theincluding target target because, multipath in this experiment,echoes, were there used isasonly clutters one because target. Thea geographically other echoes, multipathincluding echo target is multipathnot an accurate echoes, target were locator. used asIn cluttersorder to because investigate a geographically the classification multipath performance echo is ofnot the an proposed accurate algorithm, target locator. we acqu In orderired all to investigateechoes higher the than classification the noise level performance without ofa threshold the proposed for eachalgorithm, ping. we acquired all echoes higher than the noise level without a threshold for each ping.

3.2.3.2. Multilayer Multilayer Processing Processing (Feature (Feature Extraction) Extraction) WeWe introduced in-layerin-layer and and cross-layer cross-layer feature featur extractionse extractions as multilayer as multilayer processing processing for extracting for extractingfeature vectors. feature Considering vectors. Considering the complexity the complexi of underwaterty of underwater environments environments and the variationand the variation of target ofreturn target strength, return strength, it is necessary it is necessary to extract to enough extract features enough to features cover and to enrichcover and the featureenrich componentsthe feature componentsin the echo area.in the In echo some area. previous In some studies, previous researchers studies, haveresearchers found thathave when found the that number when the of features number is oflarger, features the robustnessis larger, the of therobustne recognitionss of the classiﬁer recognition is reduced classifier [25]. Theis reduced schematic [25]. of featureThe schematic extraction of is featureshown extraction in Figure2 is. shown in Figure 2.

(a) (b)

FigureFigure 2. 2. SchematicSchematic of in-layer and and cross-layer cross-layer feature feature selection selection domain. domain. (a) (3-Da) 3-D display display of echo of echo shapeshape segmentation segmentation for for in-lay in-layerer and and cross-layer cross-layer domain. domain. (b (b) )2-D 2-D display display of of data data area area for for in-layer in-layer and and cross-layer domain. cross-layer domain.

First, for the in-layer feature extraction, the dispersed energy areas of echoes are segmented into several layers, like contours, from the center peak point to the edge area (normally the noise level). Appl. Sci. 2019, 9, 4617 6 of 15

Appl.First, Sci. 2019 for, 9 the, x FOR in-layer PEER REVIEW feature extraction, the dispersed energy areas of echoes are segmented6 intoof 15 several layers, like contours, from the center peak point to the edge area (normally the noise level). In Figure 2a, the SNR of the peak point is A and the layers are defined as B, C, and D; Figure 2b shows In Figure2a, the SNR of the peak point is A and the layers are defined as B, C, and D; Figure2b shows the top view of the echo shape. The number of layers is determined by having energy distributions the top view of the echo shape. The number of layers is determined by having energy distributions of of at least 3 dB per layer by considering the dynamic range of the target echo strength; this will at least 3 dB per layer by considering the dynamic range of the target echo strength; this will depend on depend on the underwater objects present, the range of the ambient noise level according to the sea the underwater objects present, the range of the ambient noise level according to the sea state, the sea state, the sea area, and the transmitting frequency [17]. In this paper, five layers are selected in area, and the transmitting frequency [17]. In this paper, five layers are selected in consideration of the consideration of the sonar system specifications. For each layer, three feature vectors are selected. We sonar system specifications. For each layer, three feature vectors are selected. We selected the median selected the median feature to have robust performance for impulsive noise signal and representative feature to have robust performance for impulsive noise signal and representative average value of average value of echo SNR. Variance (second moment) feature vectors based on these median values echo SNR. Variance (second moment) feature vectors based on these median values are selected to are selected to represent energy distribution pattern variation. In addition to these features, we represent energy distribution pattern variation. In addition to these features, we selected skewness selected skewness feature vectors for the directional tendency of energy distribution in the bearing- feature vectors for the directional tendency of energy distribution in the bearing-range domain. Thus, range domain. Thus, 12 features for a single echo are extracted as in-layer feature vectors. Figure 3 12 features for a single echo are extracted as in-layer feature vectors. Figure3 shows examples of shows examples of different target and clutter echo 3-D shapes. Figure 3a,b and d display the multiple different target and clutter echo 3-D shapes. Figure3a,b and d display the multiple peaks of the targets. peaks of the targets. In this case, only one peak point should be extracted for precise automatic target In this case, only one peak point should be extracted for precise automatic target tracking and for sonar tracking and for sonar operator display so as not to obscure manual detection. Figure 3e shows a operator display so as not to obscure manual detection. Figure3e shows a similar pattern as target-like similar pattern as target-like clutter and Figure 3g displays multi peaks that should be classified as clutter and Figure3g displays multi peaks that should be classified as clutter. Figure3c,h display clutter. Figure c,h display target and clutter having low SNR respectively with less feature target and clutter having low SNR respectively with less feature information. To avoid unfavorable information. To avoid unfavorable situations, such as multiple peak patterns and missing target situations, such as multiple peak patterns and missing target echoes, cross-layer feature extraction is echoes, cross-layer feature extraction is applied to the system. applied to the system. In the second tier of processing, cross-layer feature vectors are developed from in-layer feature In the second tier of processing, cross-layer feature vectors are developed from in-layer feature vectors between two adjacent in-layers, for example, the B and C areas in Figure 2. The ratio of vectors between two adjacent in-layers, for example, the B and C areas in Figure2. The ratio of adjacent adjacent in-layer median values is calculated, and these features show up-slope or down-slope in-layer median values is calculated, and these features show up-slope or down-slope patterns of patterns of physical characteristics. The second moment values on a log scale are calculated in the physical characteristics. The second moment values on a log scale are calculated in the same procedure. same procedure. In order for the weak target signal to be well classified, the relationship between the In order for the weak target signal to be well classified, the relationship between the center peak point center peak point of the echo region and its surroundings should be well extracted by the feature of the echo region and its surroundings should be well extracted by the feature vectors. When the SNR vectors. When the SNR of the echo signal goes lower, the energy in the edge region disappears and of the echo signal goes lower, the energy in the edge region disappears and the energy surrounding the the energy surrounding the center point remains. This means that the loss of signal feature center point remains. This means that the loss of signal feature components may occur, and less feature components may occur, and less feature information may be extracted. Eight cross-layer feature information may be extracted. Eight cross-layer feature vectors are considered; finally, the 12 in-layer vectors are considered; finally, the 12 in-layer features plus the 8 cross-layer features for a total of 20 features plus the 8 cross-layer features for a total of 20 feature vectors in the in-layer and cross-layer feature vectors in the in-layer and cross-layer are applied. The dimension of feature vectors is not are applied. The dimension of feature vectors is not high, so the proposed algorithm can be computed high, so the proposed algorithm can be computed in real time for an active sonar system. in real time for an active sonar system.

Figure 3. Cont. Appl. Sci. 2019, 9, 4617 7 of 15 Appl. Sci. 2019, 9, x FOR PEER REVIEW 7 of 15

Figure 3. Various echo samples from sea trial data (targets and clutters). Figure 3. Various echo samples from sea trial data (targets and clutters). Figure4 shows 2-D display samples of target and clutter echoes acquired from the sea experiment and theFigure segmented 4 shows layers 2-D used display to calculate samples the of feature target information. and clutter Theechoes highest acquired peak value from is the in thesea centerexperiment rectangular and the area segmented and the segmented layers used layers to calc areulate marked the B,feature C, D, andinformation. E. The calculated The highest in-layer peak andvalue cross-layer is in the feature center valuesrectangular are listed area in and Table the1. segmented layers are marked B, C, D, and E. The calculated in-layer and cross-layer feature values are listed in Table 1. Appl.Appl. Sci. Sci. 20192019, 9,, 9x, FOR 4617 PEER REVIEW 8 of8 of15 15

Figure 4. 2-D display samples with segmented layers of the target and clutter in the experiment to Figureshow 4. feature 2-D display information. samples with segmented layers of the target and clutter in the experiment to show feature information. Table 1. Feature data in target and clutter according to Figure4.

Table 1. Feature data in target and clutterTarget according to ClutterFigure 4. A Target 24.1 Clutter 23.0 B 20.5/49.5/ 0.87 19.9/5.7/ 0.48 A 24.1 − 23.0 − In-layer feature C 10.5/70.3/ 0.02 15.4/10.6/ 0.78 − − DB 20.5/49.5/ 2.75/59.7/−0.140.87 19.9/5.7/ 10.3/31.4−/0.480.19 − In-layer feature CE 10.5/70.3/ 1.10/19.9/−0.060.02 15.4/10.6/ 3.11/70.4−/0.780.26 A,D B 2.75/59.7/0.14 1.18/4.86 10.3/31.4/ 1.16/40.3−0.19 B, C 1.95/ 1.52 1.29/ 2.69 Cross-layer feature E 1.10/19.9/0.06− 3.11/70.4/0.26− C, D 3.82/0.70 1.49/ 4.71 A, B 1.18/4.86 1.16/40.3− D, E 2.50/4.77 3.31/ 3.50 B, C 1.95/−1.52 1.29/−−2.69 Cross-layer feature C, D 3.82/0.70 1.49/−4.71 4. Experimental Results and Discussion D, E 2.50/4.77 3.31/−3.50 In this section, the proposed approach is verified with real sea trial data. As mentioned, the 4.experiments Experimental were Results conducted and Discussion with naval ships in the East Sea of Korea for several years. In order to acquire representative data from the sea area, we collected data over various sea areas, seasons, In this section, the proposed approach is verified with real sea trial data. As mentioned, the target moving patterns, water depths, sea states (from 0 to 2), and other environmental influences. We experiments were conducted with naval ships in the East Sea of Korea for several years. In order to followed and checked all of the target routes for accurate analysis. Finally, we acquired data from acquire representative data from the sea area, we collected data over various sea areas, seasons, target 1123 pings spread across all experiments. Only a single target echo from each ping was used, and other moving patterns, water depths, sea states (from 0 to 2), and other environmental influences. We echoes were regarded as clutter. We illustrate the characteristics of the proposed algorithm in scatter followed and checked all of the target routes for accurate analysis. Finally, we acquired data from diagrams of several feature vectors and quantitatively summarize the performance in classification 1123 pings spread across all experiments. Only a single target echo from each ping was used, and tables of all data. other echoes were regarded as clutter. We illustrate the characteristics of the proposed algorithm in scatter4.1. The diagrams Experiments of inseveral the Sea feature vectors and quantitatively summarize the performance in classification tables of all data. In this paper, we consider the use of an active hull-mounted sonar system to detect a moving 4.1.underwater The Experiments target. in Twothe Sea schematics of several experiment situations considered are described in Figure5. In conventional algorithms, the target detection and classification are done by comparing the In this paper, we consider the use of an active hull-mounted sonar system to detect a moving matched filter signal to a preset threshold. In this case, target and clutter signals with SNRs above underwater target. Two schematics of several experiment situations considered are described in the threshold are labeled. In long-range detection, several classification schemes are applied with Figure 5. In conventional algorithms, the target detection and classification are done by comparing appropriately high SNR signals that have enough feature information to separate target and clutter the matched filter signal to a preset threshold. In this case, target and clutter signals with SNRs above components. These schemes can increase the false alarm rate when the clutter signals are stronger than the threshold are labeled. In long-range detection, several classification schemes are applied with the target signals and also decrease the classification rate for weak target signals. It is essential to detect appropriately high SNR signals that have enough feature information to separate target and clutter targets with continuing false alarms because any target can be a threat. Thus, it is very important to components. These schemes can increase the false alarm rate when the clutter signals are stronger constantly attempt target detection even if the probability of false alarm sometimes goes up. than the target signals and also decrease the classification rate for weak target signals. It is essential to detect targets with continuing false alarms because any target can be a threat. Thus, it is very important to constantly attempt target detection even if the probability of false alarm sometimes goes up. Appl. Sci. 2019, 9, x FOR PEER REVIEW 9 of 15

The experimental data were obtained in the East Sea of South Korea in 2006, 2008, 2009, 2011, and 2012 with hull-mounted active sonar systems (DSQS21BZ and SQS-240K) operating on Korean Navy ships. The experiments were designed to cover a variety of underwater environments and general target maneuvers. We included both summer and winter data with their opposing sonic propagation characteristics and data from the beam to the bow direction to obtain a variety of target echo strengths as a function of the incident angle. The feature component analysis experiments were continuously conducted until the target signal was lower than the ambient noise. The target size was less than 100 m long; the East Sea of Korea is approximately 500 to 3000 m deep. The range to the target from ownship was about 4 to 15 kyds (approximately 3.6 to 13.7 km). The active sonar was Appl. Sci. 2019, 9, x FOR PEER REVIEW 9 of 15 operated with LFM and CW signals having a center frequency 5 to 10 kHz and a pulse length under Appl.500 Sci.ms.2019The Figure, 9experimental, 4617 5 shows examples data were of obtainedthe experi inmental the East ownship Sea of and South targ Koreaet movement in 2006, paths.2008, 2009,9 of 2011, 15 and 2012 with hull-mounted active sonar systems (DSQS21BZ and SQS-240K) operating on Korean Navy ships. The experiments were designed to cover a variety of underwater environments and general target maneuvers. We included both summer and winter data with their opposing sonic propagation characteristics and data from the beam to the bow direction to obtain a variety of target echo strengths as a function of the incident angle. The feature component analysis experiments were continuously conducted until the target signal was lower than the ambient noise. The target size was less than 100 m long; the East Sea of Korea is approximately 500 to 3000 m deep. The range to the target from ownship was about 4 to 15 kyds (approximately 3.6 to 13.7 km). The active sonar was operated with LFM and CW signals having a center frequency 5 to 10 kHz and a pulse length under 500 ms. Figure 5 shows examples of the experimental ownship and target movement paths.

Figure 5. Examples of the experimental ownship and target paths. Figure 5. Examples of the experimental ownship and target paths. The experimental data were obtained in the East Sea of South Korea in 2006, 2008, 2009, 2011, and Matched filter outputs were acquired from the signal processing unit of the experimental active 2012 with hull-mounted active sonar systems (DSQS21BZ and SQS-240K) operating on Korean Navy sonar system. First, we extracted all echoes having a peak signal above the noise level. Typically, ships. The experiments were designed to cover a variety of underwater environments and general most extracted patches are clutter because the real target is a single return, and it is especially difficult target maneuvers. We included both summer and winter data with their opposing sonic propagation to separate low SNR target signals from clutter. Echo characteristics are determined by the target size, characteristics and data from the beam to the bow direction to obtain a variety of target echo strengths target shape, ping incident angle, multipath effect, and elongation effect. In this paper, we focus on as a function of the incident angle. The feature component analysis experiments were continuously an adaptive approach for continuous detection. Normally, if the reflected signal level is low, it is conducted until the target signal was lower than the ambient noise. The target size was less than 100 m harder to separate the target from clutter because sufficient feature vectors cannot be extracted, long; the East Sea of Korea is approximately 500 to 3000 m deep. The range to the target from ownship decreasing the classification rate. In order to process low SNR target echoes, we continuously checked was about 4 to 15 kyds (approximately 3.6 to 13.7 km). The active sonar was operated with LFM and and acquire data in situations where the target echo is both lower and higher than the noise level. CW signals having a centerFigure frequency 5. Examples 5 to of 10the kHz experimental and a pulse ownship length and under target 500 paths. ms. Figure5 shows The orientation of the target, the active sonar PRI, and the GPS position and orientation of ownship examples of the experimental ownship and target movement paths. are used to confirm the target echo. Figure 6 shows one example from several experiments of a MatchedMatched filter filter outputs outputs were were acquired acquired from from the the signal sign processingal processing unit unit of theof the experimental experimental active active normalized matched filter output in the bearing-range domain. In Figure 6, the red circle indicates sonarsonar system. system. First, First, we extractedwe extracted all echoes all echoes having having a peak a signalpeak signal above theabove noise the level. noise Typically, level. Typically, most the target position; we can see that the SNR of many clutters is higher than that of the target. In this extractedmost extracted patches patches are clutter are because clutter because the real the target real is target a single is a return,single return, and it and is especially it is especially difficult difficult to case, if a preset threshold higher than the target SNR is applied, the target echo may not be detected. separateto separate low SNR low SNR target target signals signals from from clutter. clutter. Echo Echo characteristics characteristics are determinedare determined by theby the target target size, size, For this reason, a weak target echo should be classified regardless of the threshold. targettarget shape, shape, ping ping incident incident angle, angle, multipath multipath effect, effect, and elongationand elongation effect. effect. In this In paper, this paper, we focus we onfocus an on adaptivean adaptive approach approach for continuous for continuous detection. detection. Normally, Normally, if the reflected if the signal reflected level signal is low, level it is harderis low, to it is separateharder the to targetseparate from the clutter target because from clutter sufficient becaus featuree sufficient vectors cannot feature be vectors extracted, cannot decreasing be extracted, the classificationdecreasing rate. the classification In order to process rate. In loworder SNR to proces targets echoes, low SNR we target continuously echoes, we checked continuously and acquire checked dataand in situationsacquire data where in situations the target wher echoe is the both target lower echo and is higher both thanlower the and noise higher level. than The the orientation noise level. of theThe target, orientation the active of the sonar target, PRI, the and active the GPS sonar position PRI, and and the orientation GPS position of ownship and orientation are used to of confirm ownship theare target used echo. to confirm Figure6 showsthe target one exampleecho. Figure from 6 several shows experiments one example of from a normalized several matchedexperiments filter of a outputnormalized in the bearing-range matched filter domain. output In in Figure the bearing-rang6, the red circlee domain. indicates In theFigure target 6, position;the red circle we can indicates see thatthe the target SNR ofposition; many clutters we can issee higher that the than SNR that of of many the target. clutters In thisis higher case, ifthan a preset that of threshold the target. higher In this thancase, the if target a preset SNR threshold is applied, higher the target than echothe target may not SN beR is detected. applied, Forthe thistarget reason, echo amay weak not target be detected. echo shouldFor this be classified reason, a regardlessweak target of echo the threshold. should be classified regardless of the threshold.

Figure 6. One example of a sea trial matched ﬁlter output. Appl. Sci. 2019, 9, x FOR PEER REVIEW 10 of 15

Figure 6. One example of a sea trial matched filter output. Appl. Sci. 2019, 9, 4617 10 of 15

4.2. Experiment Data 4.2. ExperimentIn the experiments, Data ownship with active sonar, a single target, and a few surface ships in the vicinityIn the were experiments, employed. ownshipThe frequency with activerange of sonar, the active a single system target, was and 5–10 a fewkHz surfaceand the shipspulse inlength the vicinitywas under were 500 employed. ms for CW The and frequency LFM transmissions. range of the activeIn order system to test was the 5–10 performance, kHz and theshuffle pulse and length split wascross-validation under 500 ms was for CWused. and In total, LFM transmissions.80% of the total In data order set to were test randomly the performance, selected shu forffl traininge and split and cross-validationthe remaining data was were used. used In total, for testing. 80% of This the totalcross- datavalidation set were was randomly carried out selected 1000 times for training to evaluate and thethe remaining average detection data were performance. used for testing. This cross-validation was carried out 1000 times to evaluate the averageFor each detection ping, clutter performance. was extracted for all peak data above the noise level. We did not categorize cluttersFor eachinto similar ping, clutter groups; was we extracted simply focused for all peak on se dataparating above targets the noise from level. clutters. We did In order not categorize to extract cluttersall peak into candidates, similar groups; we scanned we simply each focusedping’s matched on separating filter output targets data from in clutters. two dimensions. In order to Since extract we allknew peak the candidates, target position we scanned and moving each ping’s route matchedfrom the filtership’s output log file, data we in labeled two dimensions. and extracted Since target we knewecho theareas target manually position from and the moving 1123 routeping fromdata thesets ship’s for an log exact file, wetarget labeled position and extractedsearch. Because target echomultipath areas manually echoes show from thetarget-like 1123 ping patterns data sets in for terms an exact of target shape position and geographical search. Because position, multipath we echoescategorized show the target-like multipath patterns echoes in as terms clutters of shape according and geographicalto their time position,delay and we the categorized known target the multipathposition. echoesIn addition, as clutters for an according evaluation to theirof weak time target delay classification, and the known we target divided position. all extracted In addition, data forinto an two evaluation groups ofas weakweak targetor strong. classification, The weak we signal divided group all was extracted defined data as the into lower two groups half of asan weak SNR- orordered strong. list The of weak the signals signal groupextracted, was and defined the stro as theng lowersignal half group of anwas SNR-ordered defined as the list ofupper the signalshalf. In extracted,Figure 7, scatter and the diagrams strong signal show groupthat in-layer was defined and cross-layer as the upper feature half. vectors In Figure can separate7, scatter targets diagrams from showclutter that efficiently. in-layer and cross-layer feature vectors can separate targets from clutter efficiently.

Figure 7. Scatter diagrams with in-layer and cross-layer features. (a) Scatter diagram of in-layer feature (varianceFigure 7. in Scatter B area) diagrams and cross-layer with in-layer feature (ratioand cross-layer of median valuefeatures. between (a) Scatter B and diagram C area); (ofb) in-layer scatter diagramfeature (variance of in-layer in feature B area) (variance and cross-layer in D area) feature and cross-layer (ratio of median feature value (ratio between of median B and value C betweenarea); (b) Cscatter and D diagram area); (c) of scatter in-layer diagram feature of (variance in-layer feature in D ar (skewnessea) and cross-layer in C area) feature and cross-layer (ratio of featuremedian (ratio value ofbetween variance C valueand D between area); (c B) andscatter Carea); diagram (d) scatterof in-layer diagram feature of in-layer(skewness feature in C (skewnessarea) and cross-layer in D area) andfeature cross-layer (ratio featureof variance (ratio value of variance between value B betweenand C area); C and (d D) area).scatter diagram of in-layer feature (skewness in D area) and cross-layer feature (ratio of variance value between C and D area). As mentioned, one of the purposes of this study was to investigate whether it is possible to detect a weakAs target. mentioned, Thus, one we of followed the purposes the history of this ofstudy the realwas targetto investigate echo SNR, whether and when it is possible the SNR to camedetect down,a weak we target. analyzed Thus, the we eﬀ ectfollowed of the featurethe history vectors. of the In real Figure target8, in-layer echo SNR, feature and #1 when is the featurethe SNR value came ofdown, the B we layer analyzed near the the peak effect of of the the echo. feature In-layer vectors. feature In Figure #2 is 8, the in-layer feature feature value of#1 theis the E layerfeature region value (edgeof the area) B layer furthest near the from peak the peak.of the Figureecho. In-layer8 shows fe thatature the #2 energy is the offeature the edge value region of the is E already layer region low, similar(edge area) to the furthest noise level, from so the there peak. is little Figure change 8 shows in the that target the energy echo’s SNR.of the Thus, edge in-layerregion is feature already #1 low, for thesimilar region to wherethe noise the level, echo energyso there is is distributed little change is proportionalin the target toecho’s the SNRSNR. of Thus, target. in-layer Figure feature8 shows #1 that in-layer features have fewer components to separate for a weak target. In contrast, the ﬁgure Appl. Sci. 2019, 9, x FOR PEER REVIEW 11 of 15

for the region where the echo energy is distributed is proportional to the SNR of target. Figure 8 Appl.shows Sci. 2019that, 9 in-layer, 4617 features have fewer components to separate for a weak target. In contrast,11 of the 15 figure shows that the peak area feature of cross-layer feature vectors does not change much when the SNR of the target goes down to the noise level. This means that the remaining portion of the energy showsstill has that some the peak feature area components feature of cross-layer to classify feature a target vectors in the does cross-layer not change features. much whenThus, the cross-layer SNR of thefeatures target goescan downdetect toa theweak noise target level. efficiently This means an thatd constantly, the remaining making portion the of target the energy continuously still has somedetectable. feature components to classify a target in the cross-layer features. Thus, cross-layer features can detect a weak target eﬃciently and constantly, making the target continuously detectable.

Figure 8. Scatter diagrams with in-layer and cross-layer features. Figure 8. Scatter diagrams with in-layer and cross-layer features. 4.3. Results and Discussions 4.3. Results and Discussions We evaluated the average classification performance with 1000 simulations using weak, strong, and allWe data. evaluated For weak the target average detection classification analysis, performance 512 weak datawith were1000 selectedsimulations from using the lowerweak, half strong, of alland data all baseddata. For on SNRweak and target 611 detection remaining analysis, data were 512 usedweakfor data strong were data.selected In total,from 1000the lower clutters half in of eachall data ping based were randomlyon SNR and selected 611 remaining from the experimental data were used data for because strong the data. number In total, of clutters1000 clutters greater in thaneach the ping noise were level randomly was huge. selected Weak from and strongthe experime cluttersntal were data selected because by the the numb sameer algorithm of clutters used greater for targetthan echoes.the noise Table level2 wasshows huge. the Weak classification and strong performance clutters were over selected all experimental by the same data, algorithm and Table used3 showsfor target the classificationechoes. Table for2 shows weak the and classification strong signals. performance In Table2, over the proposed all experimental algorithm data, is eandffective Table in3 termsshows of the both classification the classification for weak percentage and strong and signals. false alarmIn Table percentage. 2, the proposed In Table algorithm3, feature is vectorseffective trainedin terms in Tableof both2 are the tested classification for weak percentage and strong and signals, false and alarm the percentage. results are comparableIn Table 3, feature to the results vectors intrained Table2 in. The Table classification 2 are tested ratio for weak is almost and strong the same, signals, especially and the for results weak are target comparable signals. Thisto the means results thatin Table the proposed 2. The classification algorithm has ratio outstanding is almost performancethe same, especially even for for weak weak target target signals signals. and This can bemeans an effthatective the schemeproposed for algorithm active sonar has system outstanding long-range performa detectionnce even applications. for weak target signals and can be an effective scheme for active sonar system long-range detection applications. Table 2. Classification table of the proposed algorithm with the test data for 1000 simulations with allTable data. 2. Classification table of the proposed algorithm with the test data for 1000 simulations with all data. Predicted Class Predicted Class Target Clutter Target Clutter Target 95.8% 4.2% True Class Target 95.8% 4.2% True Class Clutter 0.15% 99.85% Clutter 0.15% 99.85%

Table 3. Classification table of the proposed algorithm with the test data for 1000 simulations with all data.

Predicted Class Target Clutter Appl. Sci. 2019, 9, 4617 12 of 15

Table 3. Classiﬁcation table of the proposed algorithm with the test data for 1000 simulations with all data.

Predicted Class Target Clutter Target 95.7% 4.3% Weak Clutter 0.3% 99.7% True Class Target 99.2% 0.8% Strong Clutter 1.2% 99.8%

In order to analyze the influence of strong clutters, only strong clutters were selected and trained. Thereafter, the process was the same as in the case where all data was analyzed. In Table4, the target classification rate is about 8% lower than the results of Table1, and the false alarm ratio is similar to the results of Table2. This result indicates that clutters for training should be selected according to clutter distributions of the experimental area for weak target classification; otherwise, the target classification performance can be degraded. Thus, even if there is no target in the sea, data acquisition with active sonar in the underwater environment is needed to accumulate clutter distributions. Table4 shows the average results of the classification performance when only the strong clutters are trained. In Table5, the weak and strong target classification percentages are 74.3% and 85.3%, respectively. This shows that the trained clutters affect target classification, so all of the clutters should be trained for classification performance improvement as mentioned above.

Table 4. Classiﬁcation table of the proposed algorithm with the test data for 1000 simulations (trained for only strong clutters).

Predicted Class Target Clutter Target 87.7% 12.3% True Class Clutter 0.47% 99.53%

Table 5. Classiﬁcation table of the proposed algorithm with the test data (weak and strong data) for 1000 simulations (training for only strong clutters).

Predicted Class Target Clutter Target 74.3% 25.7% Weak Clutter 0.2% 99.8% True Class Target 85.3% 14.7% Strong Clutter 0.5% 99.5%

Figure9 shows a ping history (50 pings of accumulated data) display comparing the proposed and conventional algorithms. The conventional algorithm is employed by presetting a threshold at 3 dB and extracting all signals that show the shape of the energy distribution up to the local minimum, the form of the largest peak center, and the gradually decreasing shape around the peak. This conventional algorithm is currently being applied to sonar systems and may not be the existing state of the art. We consider, however, that it is effective to compare the performance of the proposed algorithm. The situation is that the target comes toward the ownship and turns to the left during the last 10 pings. In the proposed algorithm result, target echoes are well classified and displayed. In addition, the number of clutters is greatly reduced compared with the conventional algorithm. This result shows that the proposed algorithm has a better performance in terms of the classification rate and false alarms. It can also help sonar operators greatly when detecting a target manually. Appl. Sci. 2019, 9, 4617 13 of 15 Appl. Sci. 2019, 9, x FOR PEER REVIEW 13 of 15

Appl. Sci. 2019, 9, x FOR PEER REVIEW 13 of 15

(a) Proposed algorithm

(b) Conventional algorithm

FigureFigure 9. 9.Ping Ping history history comparison comparison display display for for the the (a )(a proposed) proposed and and (b )(b conventional) conventional algorithm. algorithm. (b) Conventional algorithm For the 1123 data set, the receiver operating characteristic (ROC) curves of Tables2 and4 are Figure 9. Ping history comparison display for the (a) proposed and (b) conventional algorithm. displayed in Figure 10. The results show that case 1 has a much better performance than case 2.

Figure 10. One example of the receiver operation characteristic (ROC) curves for the cases of Table 2 (case 1) and Table 4 (case 2). Figure 10. Figure 10.One One example example of of the the receiver receiver operation operation characteristic characteristic (ROC) (ROC) curves curves for for the the cases cases of of Table Table2 2 5. Conclusions(case 1) and Table 4 (case 2). (case 1) and Table 4 (case 2). In this paper, we investigated whether targets and clutters can be distinguished with a 5.multilayer Conclusions algorithm in various environments. The proposed algorithm was applied to all data above the noise level without a preset threshold and the performance was analyzed and presented In this paper, we investigated whether targets and clutters can be distinguished with a according to weak, strong, and all data. The evaluation results confirmed that a target can be detected multilayer algorithm in various environments. The proposed algorithm was applied to all data above the noise level without a preset threshold and the performance was analyzed and presented according to weak, strong, and all data. The evaluation results confirmed that a target can be detected Appl. Sci. 2019, 9, 4617 14 of 15

5. Conclusions In this paper, we investigated whether targets and clutters can be distinguished with a multilayer algorithm in various environments. The proposed algorithm was applied to all data above the noise level without a preset threshold and the performance was analyzed and presented according to weak, strong, and all data. The evaluation results confirmed that a target can be detected and separated from clutters by the proposed algorithm in real underwater environments, even for low SNRs. Thus, the proposed algorithm is very useful and effective for underwater target classification at long ranges. Furthermore, this conclusion is based on experimental data acquired over several years via the application of a real active sonar system. Note that the experimental data may not represent all the characteristics of underwater environments, but we think that the data are sufficient to verify the possibility of applying our proposed algorithm to sonar systems; this is because the data were collected under a wide variety of field conditions, including different seasons, varied target motion, and wide sea areas. This study shows that the spatial characteristics in multilayered areas can be used as feature vectors that lead to a high classification rate with a low false alarm rate. Ping history results also indicated that the proposed approach is better than the conventional one. The ability to classify targets without the use of thresholds is a huge advantage in anti-submarine operations. Note that the proposed algorithm can be applied to all the signals without a threshold so that it attempts detection for signals that would be ignored by a conventional threshold exclusion process. Thus, the proposed algorithm can greatly improve the performance of classification and tracking, and a sonar operator’s detection capability. In addition, we believe the algorithm is practical because it is the result of using real data from an active sonar operating on a naval ship in real underwater environments. For further study, the cepstrum may be a good candidate for the feature vector component due to its scale invariance property, and the fusion of different approaches may also be a useful approach for active sonar systems.

Author Contributions: Conceptualization and algorithmology, I.S.; software, S.K.; validation, I.S. and Y.R.; formal analysis, S.K. and Y.R.; resources, S.K.; data curation, I.S. and Y.R.; writing—original draft preparation, I.S and J.P.; writing—review and editing, I.S. and D.S.H.; visualization, J.P.; supervision, D.S.H. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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