Real-Time Detection and Filtering of Chaff Clutter from Single-Polarization Doppler Radar Data

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YONG HYUN KIM AND SUNGSHIN KIM Department of Electronics and Electrical Engineering, Pusan National University, Busan, South Korea

HYE-YOUNG HAN,BOK-HAENG HEO, AND CHEOL-HWAN YOU Korea Meteorological Administration, Seoul, South Korea

(Manuscript received 1 August 2012, in ﬁnal form 26 November 2012)

ABSTRACT

In countries with frequent aerial military exercises, chaff particles that are routinely spread by military aircraft represent significant noise sources for ground-based weather radar observation. In this study, a cost- effective procedure is proposed for identifying and removing chaff echoes from single-polarization Doppler radar readings in order to enhance the reliability of observed meteorological data. The proposed quality control procedure is based on three steps: 1) spatial and temporal clustering of decomposed radar image elements, 2) extraction of the clusters’ static and time-evolution characteristics, and 3) real-time identification and removal (or censoring) of target echoes from radar data. Simulation experiments based on this procedure were conducted on site-specific ground-echo-removed weather radar data provided by the Korea Meteoro- logical Administration (KMA), from which three-dimensional (3D) reflectivity echoes covering hundreds of thousands of square kilometers of South Korean territory within an altitude range of 0.25–10 km were re- trieved. The algorithm identified and removed chaff clutter from the South Korean data with a novel decision support system at an 81% accuracy level under typical cases in which chaff and weather clusters were isolated from one another with no overlapping areas.

1. Introduction a. Problem posed by chaff echoes and the proposed approach The network of ground-based weather radars is an essential tool for real-time monitoring of rapidly de- Chaff is a radar countermeasure commonly com- veloping weather events, and in assessing the near-term posed of metallized glass fibers or other lightweight potential threat level posed by these events. Thus, the strips of reflecting material that are released by aircraft ability to accurately forecast the course of weather to distract radar-guided missiles from their targets. The events critically depends on the reliability of measure- chaff bands are very shallow and tend to show a distinct ments coming from these sources. However, weather vertical tilt from the vertical viewpoint after a given radar readings are subject to nonweather noise, such as amount of time has elapsed from the chaff’s initial re- normal ground (NG), anomalous propagation (AP), lease. In a country such as South Korea where very chaff, contrail, and hazardous clutter sources (fire, ash frequent aerial military exercises are held in a relatively plumes). These sources are often confused with weather confined air space, chaff clutters with a sizeable volume echoes, and significantly compromise the reliability of and coverage area (several hundred square kilometers) radar data. In particular, chaff clutter represents one of were observed on 231 out of 365 days in 2010 (see the main noise sources among nonweather noise echoes Fig. 1). This represents 63.3% of the total observation in countries with frequent aerial military exercises. days (Han et al. 2011). These dispersed nonweather agents easily extend to several tens of kilometers over a large area. They make it very difficult to obtain reli- Corresponding author address: Sungshin Kim, Department of Electronics and Electrical Engineering, Pusan National University, able weather condition estimates in an affected area in Jangjeon-dong, Geumjeong-gu, Busan 609-735, South Korea. the hours following the exercise—chaff remains air- E-mail: [email protected] borne for roughly 8–15 h.

DOI: 10.1175/JTECH-D-12-00158.1

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FIG. 1. KMA’s 2010 record of monthly variation of the number of days that chaff echoes are observed. (bottom) Number of days that chaff echoes were observed, and (top) their percentage of the total number of days in each month (readapted from Han et al. 2011).

As a result, cheap and robust methods for identifying and removing chaff echoes from radar data are being actively investigated by national weather forecasting FIG. 2. The network of ground-based radars used by the KMA. agencies in a number of countries in order to improve the quality of meteorological data. However, difficulty arises from the fact that with a current nationwide network (such as average intensity, storm size, velocity variance, of single-polarization-based C- and S-band Doppler radars storm shape, and orientation) to be tracked over time installed in the majority of affected weather observation in addition to the storm center’s location. Johnson et al. centers, residual chaff clutter is not easily distinguished (1998) used an enhanced Weather Surveillance Radar- from weather echoes because of their overlapping reflec- 1988 Doppler (WSR-88D) centroid tracking algorithm tivity range. called Storm Cell Identification and Tracking (SCIT) to In an attempt to solve this problem, analyses of time track and identify storm cells (isolated, clustered, or series 3D radar constant altitude plan position indicator line storms) at various levels of maximum threshold (CAPPI) data are being conducted in this study to iden- reflectivity. tify key discriminating characteristics that can differen- c. Previous works on chaff characterization tiate chaff echoes from weather echoes to a reasonable degree of accuracy. Previous works on chaff characterization are primarily concerned with monostatic and bistatic radar b. Previous works on tracking amorphous cross sections (RCSs). For monostatic RCSs, Marcus weather objects (2004) developed a chaff model based on aerodynamic There are several algorithms described in the current principles that determines density and orientation dis- literature that are designed to automatically track de- tributions of reflective fibers within chaff clutters. For formable weather structures (usually storm cells). They bistatic RCSs, Guo and Uberall€ (1992) proposed a include a spatial relaxation-labeling algorithm (Barnard variational method of computing radar-scattering cross and Thompson 1980) that uses disparity analysis to de- sections for a bistatic-scattering cross section for chaff termine the correspondence between a set of feature dipoles. points selected from a stereogram, and a temporal re- The terminal chaff fall velocity was calculated by es- laxation algorithm (Zhang 1991) that tracks Euclidean timating a chaff drag coefficient as described by Jiusto storm centers over time and handles the merging and and Eadie (1963). Vasiloff and Struthwolf (1997) briefly splittingofstormsbyallowingsinglestormstobematched detailed the time-evolution characteristics of chaff echoes to several storms in previous or subsequent image frames. and their dissipation pattern within convective cloud cells, Krezeski et al. (1994) improved Zhang’s tracking algo- resulting in inaccurate estimation of the amount of pre- rithm by adding the concept of pseudostorms and property cipitation for observed cloud structures. Through the coherence, which allows multiple features of a storm analysis of the chaff’s polarimetric properties, Zrnicand

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FIG. 3. Proposed CEDR QC algorithm.

Ryzhkov (2004) concluded that polarimetric radars could 2. System overview provide a simple and effective way to detect chaff based a. Radar data used on experimental evidence obtained with circularly polar- ized radars. The Korea Meteorological Administration (KMA) primarily collects weather information from 11 ground- d. Previous works on identification of various types based radar observation centers with a combined coverage of radar clutters area large enough to cover the entire South Korean pen- In the past several years, various techniques have been insula, as illustrated in Fig. 2. Three of these centers— proposed on radar data quality control (QC). Many of Baengyeongdo, Youngjongdo, and Myeonbongsan—use these studies were primarily concerned with the detec- single-polarization C-band Doppler radars. Eight other tion of NG and AP clutter (Pratte et al. 1993; Pamment centers use single-polarization S-band Doppler radars. and Conway 1998; Grecu and Krajewski 1999; Kessinger The observation range for each site varies between 200 and et al. 1999; Steiner and Smith 2002; Berenguer et al. 2006; 256 km. Radars at each site feature 10–16 elevation angles Cho et al. 2006; Lakshmanan et al. 2007; Hubbert et al. for each sweep and generate 3D radar volume data in 2009), while others were concerned with the detection of universal format (UF) at 10-min intervals. The radar vol- less routinely occurring hazardous clutter, such as fire ume data used within UF data structures for the QC pro- plumes (Rogers and Brown 1977; Melnikov et al. 2008) cedure are ground echoes-removed (CZ) reflectivity data and volcanic ash clutter (Marzano et al. 2006). Many of that are the by-product of Gaussian model adaptive pro- these detection techniques adopt in some way fuzzy in- cessing (GMAP) and infinite impulse response (IIR) fil- ference (FI) or neural network (NN)-based clutter dis- tering (Siggia and Passarelli 2004). From these volume crimination schemes at the range gate level of one or data, 3D CAPPI data with 1-km horizontal and 0.25-km more radar moments (reflectivity, velocity, spectrum vertical resolution are constructed for an altitude range of width, etc.). In addition, many of these studies apply 0.25–10 km. Our study features two test simulation cases a more holistic and crisp approach of filtering data using dated 3 and 15 June 2010 at the Oseongsan (KSN) site radar moment thresholding during the preprocessing and that were chosen among 116 cases representing different postprocessing QC stages. sample dates, times, and sites between 2010 and 2011.

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FIG. 4. 2D example of NS clustering procedure with hypersphere (dotted square) scan radius of 1 grid distance resulting in three clusters.

Since weather events are monitored in real time at procedures (these procedures apply to all time series 10-min intervals, the QC procedure for all 11 sites must images or frames within a user-defined time period) be- be completed within the short time frame of 10 min or fore being subjected to the image element tracking and less. The algorithm thus takes into account this time re- inferring procedures designed to detect and remove chaff striction and limited computational resources that impose signatures. significant constraints on the choice of QC model and As a first step, the algorithm performs a coordinate representing procedures. system transformation of the original site-specific reflectivity volume data from polar to Euclidean coordinates b. Proposed chaff echo detection and removal before subjecting these data to an image decomposition (CEDR) QC algorithm (data partitioning) procedure. This transformation is In the proposed QC algorithm, illustrated in Fig. 3, the achieved by retrieving interpolated 2D CAPPI layers original time series radar data undergo a series of from original CZ reflectivity volume data for regularly transformations, sorting, and image element regrouping spaced altitudes. The layers are then stacked one upon

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TABLE 1. Simulation environment and parameters for NS TABLE 2. Simulation time measurement of NS clustering. clustering. Total Average Environment settings clustering clustering and parameters Description and values Radar sample date time (s) time (s) CPU Intel Core i5 M480 2.67 GHz KSN 3 Jun 2010 (1000–1300 KST 7.79 0.41 RAM 3072 MB at 10-min intervals for a total of OS Linux Ubuntu 10.10 19 inference time frames) Radar polarization Horizontal KSN 15 Jun 2010 (1000–1300 KST 6.27 0.33 type at 10-min intervals for a total Test radar site KSN (Oseongsan) of 19 inference time frames) Size of LS R 3 u 3 f: 980 3 360 3 15 (total: 5 292 000 bytes) Size of LE X 3 Y 3 Z: 480 3 480 3 41 (total: 9 446 400 bytes) the temporal clustering procedure forms an isocluster L 3 3 3 3 Resolution of E X Y Z:1km 1km 0.25 km subspace where member elements are 2-tuple cluster Zth 0dBZ 3 identiﬁers (t, k) for clusters in different time frames that Sth 190 grid data points or 47.5 km tk 2 grid distance are determined to be similar by criteria of cluster size and cluster centroid location (t denotes a time frame index and k denotes cluster ID). The establishment of the correspondence between clusters in subsequent 3D radar im- the other, thereby resulting in 3D Euclidean reﬂectivity ages enables the retrieval of clusters’ time-evolution

(corrected) volume data (LE). characteristic profiles, provided that the number of asso- Subsequently, a preliminary data sorting procedure is ciated (linked) clusters between image frames is above applied to all individual data points constituting this newly a threshold minimum (Rth). Otherwise, the inference sys- created volume data. The procedure categorizes these data tem for target cluster identification is not applied to these into two reflectivity range groups: below-threshold and disjoint normal clusters. above-threshold reflectivity groups. Threshold reflectivity Retrievable dynamic characteristic profiles include

Zth is chosen between a 230- and 5-dBZ value range a cluster’s time-dependent altitude (the z component of (the default is 0 dBZ); typical chaff clutters exhibit re- the cluster centroid), its size expansion (the volume or flectivity within a 10- and 35-dBZ value range. This step projection area) profile, and the mean reflectivity pro- can dramatically speed up the overall tracking procedure file. The static characteristic profiles include the long- by limiting the clustering procedure only to data points ness factor (the degree to which a cluster looks long and in the selected group. The output of this sorting pro- thin) for a fixed time frame. cedure is a set of 3-tuple data points (x, y, z)thathas Regardless of whether a cluster being probed is a chaff reflectivity values above a threshold minimum. This cluster, it is then inferred based on these characteristic sparse set (B) and the 3D Euclidean reflectivity volume profiles. We note that among these retrievable profiles, data (LE) are then passed as inputs to the spatial clus- a cluster’s time-dependent altitude profile, from which a tering procedure. particle’s altitude-averaged fall velocity can be calcu- The spatial clustering procedure decomposes the sparse lated, is of particular importance. This is because a clus- set B (sparse in the sense that B is selected from larger ter’s structure can be directly inferred from this profile input dataset) into multiple subsetsorpartitionsbyadis- because of the correlation that exists between the size of tance proximity criteria. Spatial clustering used in this particles making up the cluster and the particles’ time- model does not require specifying the number of clusters averaged fall velocity (Mitchell 1996; Jiusto and Eadie a priori. Resulting partitions from this procedure form 1963; Matrosov et al. 2002). individual cluster sets containing spatially connected or This means that by observing a cluster’s vertical move- neighboring data points. Once the spatial clustering pro- ment profile alone, one can determine with reasonable cedure that is repeated for all time series images within confidence whether a cluster is chaff. Chaff clusters exhibit a specified inference time period is completed, in order gradually decreasing altitude levels, whereas most typical to further reduce the time taken by the overall tracking weather-related clusters maintain uniform or a somewhat algorithm, the size of each cluster (the cardinality of the irregularly fluctuating altitude level, provided that there is cluster set) formed from the previous step is computed. no rapid storm surge situation or other situations that re- Clusters only in selected size categories are chosen for the sult in significant vertical air movement. Once chaff clut- temporal clustering procedure, which associates identical or ters have been identified using the proposed inference similar clusters between time series images. The output of method, their coordinates undergo a transformation

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FIG. 5. 2D and 3D radar image decomposition for KSN 1130 KST 3 Jun 2010 data: (left) top view and (right) side angle view. (a) Full echoes. (b) Below-threshold reﬂectivity (,0dBZ) echoes removed. (c) Below-threshold re- ﬂectivity (,0dBZ) echoes and below-threshold size (,190 grid points or 47.5 km3) clusters removed.

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FIG. 6. 2D top view of radar image decomposition for KSN 1230 KST 15 Jun 2010 data. (a) Full echoes. (b) Below- threshold reflectivity (,0dBZ) echoes removed. (c) Below-threshold reflectivity (,0dBZ) echoes and below threshold size (,190 grid points or 47.5 km3) clusters removed. (d) Target normal cluster being probed (later identified as chaff cluster).

procedure (back to polar coordinates) and are subse- 3. Spatial clustering of radar echo image elements quently removed from the original L reflectivity volume S a. Description of neighborhood-scan (NS) data to generate chaff-removed L data (L0 ). S S clustering method The proposed method has a limitation in its ability to detect chaff echoes at an early stage of their development Various clustering techniques are used in remote sens- because there are not enough data points from which key ing applications and pattern recognition problems to per- dynamic profiles can be extrapolated. The method also form image decomposition prior to a feature extraction has a limitation of when chaff clutter overlaps with other procedure. The clustering procedures essentially partition weather echoes, since the mixed presence of chaff clutter a finite number of objects into a finite number of groups, cannot be resolved or discriminated with CZ reflectivity so that any two objects belonging to the same group are information alone, as previously stated. Hence, chaff more similar than those belonging to different groups. This clutters have the best chance of being detected in clear- section describes the NS image data clustering method that sky conditions with no interfering amorphous weather immediately follows the step (in Fig. 3) that filters out the structures. above-threshold reflectivity data.

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FIG. 7. Example of EL clustering procedure with T 5 6 resulting in six isocluster sets. Here, T is the total number of inference time frames and NC is the number of the similarity comparisons performed at each step. Total number of similarity comparison performed in this example is 49. Starting from the last time frame t6, each normal cluster in this frame is compared to normal clusters in the immediately preceding time frame t5. Clusters with minimum dissimilarity are then linked together as a result of this comparison. Sub- sequently, starting from linked cluster in t5, the same comparison procedure is repeated with clusters in the immediately preceding time frame until a cluster is no longer able to ﬁnd a matching cluster from the preceding time frame. Procedure then goes back with the remaining clusters in t6, and the whole procedure is repeated until there are no more clusters left with unassigned isocluster membership in all inference time frames.

The NS clustering method shown in Fig. 4 is loosely if it is insufﬁciently close to any of the existing clusters. based on the single-pass method or the leader method However, the proposed method diverges from the single- (Karkk€ ainen€ and Franti€ 2007), in that all elements (data pass method in its implementation and time complexity. points) are treated as individual clusters at the start and The single-pass and NS methods are of O(n2)andO(Mn) that at each iteration step, each element (data point) is time complexity, respectively, where M is the number of compared to clusters formed thus far, and is either added data points enclosed by a local neighborhood volume to the closest cluster or is used to start (lead) a new cluster space (simply termed hypersphere)tobescannedandn is

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FIG. 9. ANFIS for the determination of the degree of similarity between two temporally separated clusters. The zeroth layer of this NN represents two fuzzy input variables: d and h. First layer represents linguistic values associated with each of these two input variables (IF-part). Second and third layers represent rules and norm (AND-logic with attribution of weights for each rule). Fourth layer represents the THEN-part of the rules where each rule makes their own contribution to the overall output. Fifth and sixth layers represent weight normalization of the output and the ﬁnal output itself.

requirement imposed by the QC system (50 s or less for each incoming radar site data), the algorithm was run

FIG. 8. Two input fuzzy variables, d and h, in the antecedent with for 19 successive time frames with the simulation envi- their MFs to represent different degrees of the similarity between ronment settings and parameters shown in Table 1. The two temporally separated clusters. average clustering time per time frame was then calculated by taking the ratio of the total clustering time the total number of input data points for which location taken by all frames to the number of frames. The result similarity comparisons are performed. is shown in Table 2. In NS clustering, each element (data point) in the input The overall data partition or decomposition of a 3D sparse set B, which points to a specific volume element in radar image that results from reflectivity range and size range filtering is illustrated in Figs. 5 and 6. LE, is checked once (with negligible processing time) for its cluster membership. If no cluster membership is previously assigned to the element, then the element is used 4. Temporal clustering of radar echo image to start a new cluster. For each element successively elements added to the new cluster set, a neighborhood volume scan a. Description of evolution-linkage (EL) is performed to detect and to add more neighboring data clustering method points having reflectivity above a threshold minimum (i.e., .0dBZ), provided that these neighboring data points The EL clustering procedure is in essence an object- have not already been added to the cluster set (added or tracking procedure that associates similar clusters between flagged members are prevented from being added again). images to form an isocluster set, as defined in section 2b. Once no additional members are detected and added The tracking procedure is fundamentally based on the as- to the current cluster set, the process moves to the next sumption that it is substantially unlikely that a tracked ob- cluster and the same procedure is repeated until all ele- ject will undergo large displacement within the relatively ments (data points) in the input sparse set B are processed. smalltimewindowdefinedbytheimageframerate.The The key feature of this algorithm is that the neigh- EL clustering procedure, an example of which is shown borhood volume scan for the proximity check of above- threshold reflectivity data elements is limited to data points within the hypersphere and to data points that TABLE 3. Simulation time measurement of EL clustering. have not yet been flagged (assigned a membership). Total These key features dramatically improve the speed of clustering the clustering procedure. Radar sample date time (s) b. Simulation result of NS clustering KSN 3 Jun 2010 (1000–1300 KST at 10-min intervals 0.17 for a total of 19 inference time frames) To evaluate the performance of the NS clustering KSN 15 Jun 2010 (1000–1300 KST at 10-min intervals 0.23 method and to determine if it meets the execution time for a total of 19 inference time frames)

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FIG. 10. Time series images of selected target cluster for KSN 3 Jun 2010: (left) top view and (right) side angle view for (a) 1010, (b) 1040, (c) 1110, and (d) 1210 KST.

Unauthenticated | Downloaded 10/08/21 04:20 AM UTC MAY 2013 K I M E T A L . 883 in Fig. 7, uses the distance that separates clusters’ centroids TABLE 4. Notation used throughout section 5a for vertical and clusters’ size difference (either volume or the planar movement profile. projection area difference) as thebasecriterionforasimi- Symbol Description larity comparison. Other arbitrary features such as a mor- t Time (s) phological feature or mean reflectivity can be used as 21 yc Chaff fall velocity (m s ) 21 comparison criteria. However, for the simultaneous moni- yT Terminal chaff fall velocity (m s ) toring of many clusters at optimum frame rates, these fea- mc Chaff particle mass (kg) tures could be redundant. The EL clustering procedure has g Acceleration of a body within the earth’s 22 2 2 1 2 gravitational field (g 5 9.8 m s ) O[(T 1)(q q)/2] or simply O(q ) time complexity 23 rair Air density (kg m ) (considering the worst-case scenario, where the cluster- 23 rc Chaff density (kg m ) 2 tracking process goes all the way to the first frame), where fc Chaff cross-sectional area (m ) q is the average count of normal clusters within each time kc Chaff drag coefficient frame and T is the number of inference time frames. gR Reynolds number dc Chaff dipole diameter (m) The algorithm essentially makes linkage chains connect- 21 mair Dynamic viscosity of air (s kg m ) ing similar clusters (represented by dot-connected cells in P Pressure (Pa) Fig. 7) in adjacent time frames until there are no more T Temperature (K) 2 2 similarclusterslefttobeconnected (the clusters are tracked R Ideal gas constant (R 5 8.314 47 J mol 1 K 1) 5 21 as long as they do not undergo large displacement, size mair Molar mass of dry air (mair 0.028 964 4 kg mol ) 5 21 expansion, or shrinkage). Linkages are formed starting z Temperature lapse rate (z 0.0065 K m ) P Sea level standard atmospheric pressure from the last time frame and continuing in reverse time 0 (P0 5 101.325 kPa) order. At each linkage junction, a selected cluster at T0 Sea level standard temperature (T0 5 288.15 K) a particular time frame is compared with all clusters in ha Altitude (km) the immediately preceding time frame and the one that ap p/21 most closely matches is linked. This process is repeated until every cluster within the inference time period is The size difference between two clusters is de- assigned an isocluster membership. No two clusters termined by h 5 1 2 minfV1, V2g/maxfV1, V2g,where within a single time frame will have the same isocluster V1, V2 is the cluster’s respective volume (or planar membership. projection area). These two variables constitute fuzzy variables with their membership functions (MFs), as b. Similarity measure shown in Fig. 8.

Given two clusters C1,C2 in two adjacent time frames Based on these two fuzzy variables, the following two- and their respective centroids p1 and p2, the distance that input single-output Takagi–Sugeno–Kang (TSK) fuzzy separates these two centroids is given by d 5 kp1 2 p2k. model with four rules is constructed:

: 21 1 : , , : 5 0 5f2 d 0 5h 0 d f2 R1 If d is small and h is negligible, then G . 1 d f2 : 21 1 : , , : 5 0 5f2 d 0 50 d f2 R2 If d is small and h is significant, then G . 1 d f2 : 5 : 1 : R3 If d is large and h is negligible, then G 0 5 0 5h : 5 R4 If d is large and h is significant, then G 1, where the small and large are two linguistic values at- preceding TSK fuzzy system can be represented as an tributed to d representing small and large distance, and adaptive neuro–fuzzy inference system (ANFIS), as shown the negligible and significant are two linguistic values in Fig. 9. attributed to h representing the negligible and signifi- c. Simulation results of EL clustering cant size difference between clusters. In this model, the output (G 2 [0, 1]) represents the Performance tests of EL clustering were carried out degree of dissimilarity between two clusters. The nearer with same simulation environment settings and param- the degree is to its maximum unity value, the less similar eters shown in Table 1 for a period defined by 19 in- the two clusters are. Since each rule has a crisp output, the ference time frames. KSN radar site data dated 3 June overall output is obtained via the weighted average. The and 15 June 2010 were used for the test simulation. The

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FIG. 11. Vertical movement profiles of target clusters (subsequently identified as chaff). (a) Mean altitude (left) and fall velocity (right) profile of the centroid of the cluster in Fig. 10 for KSN 3 Jun 2010. (b) Mean altitude (left) and fall velocity (right) profile of the centroid of the cluster in Fig. 6d for KSN 15 Jun 2010.

simulation time measurements of EL clustering are typical weather clusters that maintain at a relatively con- shown in Table 3. The time-evolution images of a target stant altitude under normal weather conditions. cluster, characteristics of which are yet to be determined, Chaff dipoles, similar to any other particles in a fluid are shown in Fig. 10. environment, obey the following equation of motion (the notations for variables used throughout section 5a are shown in Table 4): 5. Profiling of cluster characteristics dy a. Vertical movement profile m c 5 m g 2 (1/2)k f r y2 , (1) c dt c c c air c Like any airborne solid body with a bulk density that is greater than the air density, chaff particles with a typical where the chaff drag coefficient kc (Jiusto and Eadie 5 23 nominal density of rc 2.34 g cm or higher are char- 1963) is derived by assuming the chaff dipole to be acterized by their gradual descent within the atmosphere. a horizontally oriented cylinder with infinite length and

Speciﬁc fall velocities are dependent on the air density, kc is related to gR by the following power law: air viscosity, particle density, and particle size. Although chaff particles are made as light and small as possible 2 k 5 jg q (0:5 # g # 10), (2) so they can remain in the air for a signiﬁcant length of c R R time, they eventually fall to the ground. This is one key characteristic that differentiates chaff clusters from other where j 5 10.5 and q 5 0.63.

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FIG. 12. Size expansion profiles of target clusters (subsequently identified as chaff). (a) Volume [(left) slope 5 2 2 1.33 3 104 km3 h 1] and coverage area [(right) slope 5 3.33 3 103 km2 h 1] profile of the cluster in Fig. 10 for 2 KSN 3 Jun 2010. (b) Volume [(left) slope for 1000–1220 KST interval 5 3.75 3 103 km3 h 1]andcoverage 2 area [(right) slope for 1000–1220 KST interval 5 1.35 3 103 km2 h 1] profile of the cluster in Fig. 6d for KSN 15 Jun 2010.

The Reynolds number g is given by R The air density can also be related to altitude variation by the following expressions: 5 21 gR (mair )rairdcyT . (3) (m g/Rz) m P zh air r 5 air 0 1 2 a (6) The terminal velocity of the chaff dipole can then be air RT T derived from (1) to (3) as 0 : r T 0 19 h 5 4:43 3 104 1 2 air . (7) 5 1:2 0:3 0:5 21 0:7 a 353:4 yT dc (rair mair ) (apgrc) . (4)

Terminal chaff fall velocities have previously been Note that the terminal velocity is the velocity attained measured as a function of air density, air viscosity (Jiusto when dy /dt becomes zero. Changing the thermody- c and Eadie 1963), and altitude for a ﬁxed chaff dipole namic environment (the air becomes less dense at higher density and diameter (i.e., yT takes values in the range altitude) requires that the air density be corrected for 21 23 of 0.25–0.26 m s for rair ’ 0.8 kg m , mair ’ 1.7 3 pressure and temperature using the ideal gas law: 25 21 10 skgm , T ’ 262 K, dc 5 28.4 mm, rc 5 2 2.34gcm 3,andh ’ 4.0–6.0 km). The vertical move- 5 a rair mairP/RT . (5) ment proﬁles of target clusters obtained using the

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characterized by their continually expanding volume (or projection coverage area) from a very early stage of their life cycles. Then, they slowly shrink in a ﬁnal collapsing stage as particles start to hit the ground in increasing numbers. The chaff dipoles are tiny cylindrical solid particles. They are not subject to thermal expansion and contraction like air or water vapor molecules. Their con- tinual expansion or spreading is mainly due to the simple effect of gravity pulling them downward and the random movement of air, which carries them around and dis- perses them in all directions. Two parameters that are alternatively used to determine the size of a given cluster are the volume and the projection coverage area. Here, the volume is the total number of data points within cluster set C (the cardinality of cluster set C) divided by a z-direction scaling factor. The projection coverage area is the total number

of data points within the Cp set, which is defined as Cp 5 projU(C), where U is the 2D horizontal plane upon which cluster C is projected. The size expansion profiles of the target clusters featured in Figs. 10 and 6d are shown in Fig. 12. c. Mean reflectivity profile and longness factor Mean reflectivity time series profiles, as shown in Fig. 13 (featuring two selected clusters in Figs. 10 and 6d), can be used to identify, within a reasonable accuracy, potential candidate chaff clusters. This is because clusters composed of tiny solid particles such as chaff characteristically exhibit gradually decreasing mean reflectivity values due to their decreasing bulk density as solid particles continually fall and spread out in the air. The longness factor is a measure of the degree to which FIG. 13. Mean reflectivity profiles of target clusters (sub- a given cluster looks long and thin when viewed from sequently identified as chaff). (a) Mean reflectivity profile of the the top. It is valued between 0 and 1, 1 being maximally cluster in Fig. 10 for KSN 3 Jun 2010 (least squares slope 520.0275 2 or infinitely long and thin, and 0 being the opposite. Al- dBZ min 1). (b) Mean reflectivity profile of the cluster in Fig. 6d 2 though the longness factor alone does not conclusively for KSN 15 Jun 2010 (least squares slope 520.0386 dBZ min 1). tell whether a given cluster is chaff, it does weigh-in on the identification process: chaff clusters tend to exhibit proposed method are shown in Fig. 11. Here, the z a value in the upper half of the [0, 1] range. The geometry componentofthecluster’scentroidisusedtotrackits and calculation procedure of the longness factor are shown vertical movement. in Figs. 14 and 15, respectively. The resulting longness factor profiles of the selected clusters featured in Figs. 10 b. Size expansion profile and 6d are shown in Fig. 16. The size expansion profile of a given cluster, in essence, provides information on the extent of the dis- persion of the cluster’s constituent particles over time, 6. Inference system for chaff echoes detection from which various deductions concerning the nature of a. Description of inference system the cluster can be drawn. Unlike typical amorphous cloud structures in a nonstorm surge situation that ir- The inference system for the detection of chaff ech- regularly undergo a succession of relatively mild ex- oes uses three dynamic characteristic parameters and pansion and contraction processes, chaff clusters are one static characteristic parameter as input variables.

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FIG. 14. (a) Projection operation of 3D cluster in 2D plane using KSN 1300 KST 3 Jun 2010 data. (b) Geometry for the calculation procedure of the longness factor.

, ! The three dynamic characteristic parameters are the 4 4 5 5 å å cluster’s fall trend index hf, volume-expansion trend FI F(Y) F Qixi Qi , i51 i51 index he, and mean reflectivity trend index hr.The 5 1 1 1 static characteristic parameter includes only the long- F(q1 hf q2 he q3 hr q4 gk), (8) ness factor gk. The calculations of hf, he,andhr are performed using the algorithms shown in Fig. 17. All where xi 2 [0, 1] are the input variables, and Qi and qi 2 four parameters (hf, he, hr,andgk) have values in the [0, 1] are the individual weights and normalized in- range of [0, 1]. dividual weights associated with each input variable, Based on these input variables, we constructed a sim- respectively. Note that the intermediate value Y also has ple four-input single-output single-layer feed-forward the range of [0, 1]. The final decision that segregates perceptron (Rosenblatt 1958) NN, as shown in Fig. 18. chaff from nonchaff clusters is based on the following A single-layer perceptron NN, which is similar to the activation function: Adaline NN by Widrow and Hoff (1960), has an ad- vantage over other NN models for its extreme simplicity 1ifY .Y F 5 th , (9) and relatively small number of computations required I 0 otherwise for an inference and distributed online learning procedure that helps to speed up the CEDR QC procedure. where Yth is the threshold output level of the NN. If The output of a CEDR QC NN is the activation of a the final output FI is 1, then the sample cluster will be weighted linear combination of the input parameters assigned to class chaff. If the final output is 0, then the (neurons), and is given by pattern sample cluster will be assigned to class nonchaff.

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FIG. 15. Flowchart of the longness factor calculation procedure. Algorithm simply calculates, in three rotated frames (one along normal axis, two along diagonal axis), the ratio of the width (fraction of the unity) to the length (always normalized to the unity) of rectangles enclosing a cluster. After the minimum of the three ratios is subtracted from the unity, the algorithm takes this difference as the characteristic longness factor of the target cluster.

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computed to add a correction to the old value in the following way: 1 5 1D qij(t 1) qij(t) qij(t) (10) 1 5 1D Yth,j(t 1) Yth,j(t) Yth,j(t). (11) However, since there can be as many as 105 combinations of weights and threshold output levels for each

pattern if Dqij and DYth,j are both set to 0.1 (as an example), the convergence to optimally adjusted qi and Yth can take considerable time and cannot feasibly be implemented in the proposed QC optimization procedure.

Thus, only heuristically selected combination sets of qi and Yth are considered for the optimization task (i.e., q1 5 0.4, q2 5 0.3, q3 5 0.2, q4 5 0.1, and Yth 5 0.6; q1 5 0.3, q2 5 0.3, q3 5 0.2, q4 5 0.2, and Yth 5 0.7; q1 5 0.5, q2 5 0.2, q3 5 0.2, q4 5 0.1, and Yth 5 0.7; etc.). The perceptron learning rule used for the training can thus be stated as follows:

Given Hq 5 fc1, c2, ..., cmg, a set of different combinations of qi and Yth (denoted cu) for the connections, where m is the total number of combinations, 1) n ) 1; u ) 1; j ) 1. 2) Obtain input vectors x for different clusters from nth sample (at time t)ofjth pattern.

3) Select cu and compute FI,nj for different clusters of nth sample of jth pattern.

4) Compute PI,nj. 5) n ) n 1 1. 6) If n # L (where L is the number of training sample data points of jth pattern), then Goto step 2; else Goto step 7.

7) Compute average PI,nj over n for jth pattern. $ FIG. 16. Longness factor profile of target clusters (subsequently 8) If average PI,nj PID, then Goto step 12; else Goto identified as chaff). (a) Longness factor profile of the cluster in step 9. Fig. 10 for KSN 3 Jun 2010. (b) Longness factor profile of the 9) n ) 1; u ) u 1 1; j ) j 1 1. cluster in Fig. 6d for KSN 15 Jun 2010. The generally constant 10) If u # m, then Goto step 2; else Goto step 11. level of longness factor shown in (a) and (b) can be attributed to the fact that although chaff is spreading mostly along the direction 11) Select cu for which average PI,nj is maximum of the aircraft’s flight path, it is also spreading radially with the (converging point). Goto step 13. random movement of the air carrying its particles. 12) Select cu as the converging point of qi and Yth combinations. 13) End. NN online (pattern by pattern) learning is achieved by b. Simulation result of chaff echoes detection a weight and threshold output level adjustment at each NN evaluation pattern using the perceptron learning The test simulation for chaff echo detection based on rule. Here, each jth pattern consists of a set of L training the aforementioned NN system was carried out using sample data points (at least 6 h of time-separated radar KSN radar site data dated 3 June 2010. The result is data) for which the jth average detection performance shown in Fig. 19 and Table 5. index PI,j (described in the next section) is calculated. Each isocluster represented in Table 5 (see Fig. 19 for If PI,j is found to be less than the desired performance matching isocluster locations) is generated by associat- index PID, which is set to 0.8 by default, then the new ing similar clusters between images in reverse time frame value for each weight and threshold output level is order starting at 1300 until 1000 Korean standard time

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FIG. 17. Algorithms for calculation of (a) fall trend index, (b) volume-expansion trend index, and (c) mean reflectivity trend index. Algorithms described in (a) and (b) count the number of beyond- threshold-level altitude drop instances (Kf) and volume-expansion instances (Ke) within the inference time frames. If the instance count is above a threshold, then the tracked cluster is assigned a trend degree that is the ratio of the instance count of dynamic state change to the total number of the inference time frames. Otherwise, it is assigned a value of zero. Algorithm described in (c) estimates the slope of least squares regression line fitting the mean reflectivity profile. If the slope is found within a particular range (a negative interval), then the cluster is assigned a triangularly

shaped trend degree with its peak (a unity) at Sr2. Otherwise, it is assigned a value of zero.

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FIG. 18. Single-layer perceptron network with threshold activation function for binary classiﬁcation of radar echo clusters (FI 5 0 for nonchaff; FI 5 1forchaff).

(KST) at 10-min intervals for a total of 19 time frames. In Table 5, the longness factor corresponding to a speciﬁc isocluster ID is attributed to the last time frame cluster belonging in the same isocluster set (in this case, the cluster at 1300 KST). Other single-frame cluster statistics for the 1300 KST 3 June 2010 data are shown in Table 6.

The performance index of chaff echo detection PI is the measure of the success rate of a correct identiﬁcation using the proposed method, and is expressed by

D D 1 D P 5 1 2 T 5 1 2 c nc , (12) I jBj jBj where Dc is the total number of data points (or cells) that are wrongly identified as chaff, Dnc is the total number of data points (or cells) that are wrongly identified as nonchaff, and jBj is the cardinality of the set of input data points that have above-threshold reflectivity as FIG. 19. (a) 2D top view of normal clusters for KSN 1300 KST 3 defined in the previous section. Jun 2010 data and their corresponding isocluster membership The actual calculation of the performance index is ID. (b) Identified chaff clusters by the proposed method with performed by considering two separate cases: one is the parameter settings displayed in Table 5. case in which it is known that only nonchaff echoes are present, and the other is the case in which both chaff and nonchaff echoes are known to be present. The The calculation procedure of the performance index is information that reveals whether a given radar image shown in Fig. 20. Performance index (PI) has a range represents a chaff-contaminated case is provided by between 0 and 1. The closer the index is to 1, the better KMA experts who can identify nonweather (such as the results are. A compilation of case-by-case perfor- chaff) from weather echoes based on their first-hand mance results of the overall chaff echo detection pro- knowledge and experience, and also with the assis- cedure is illustrated in Table 7. tance of specialized cross-referencing radar image The overall performance index of 0.81 shown in analysis tools (Han et al. 2011). The radar image ar- Table 7 indicates that chaff clutter can be detected with chives in which nonweather clutter (such as chaff) are fair accuracy (within an acceptable level) despite the separately labeled (censored) from regular weather fact that there is no obvious way to choose a single set of echo are provided by the KMA after the long analysis optimal parameters (the training routine is only based session is completed. These radar data are essentially on few heuristically selected parameter settings) for the required for the performance evaluation of the proposed inference system that is applicable to all cases. We also technique because they establish independent referential note that this result is significantly influenced by the way truth to which the inference results of the proposed the KMA experts identify the chaff clutter that is used as method can be compared. the truth reference. The following is the list of some key

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TABLE 5. Results of inference system based on single-layer perceptron NN with activation function applied to normal clusters in KSN 1300 KST 3 Jun 2010 data. (Correctly identiﬁed chaff by cluster number: 75%, by volume: 99.3%. The results below are obtained with 21 21 the following parameter settings: Rth 5 4, h1 5 0.055 km, h2 5 0.270 km, Uth 5 0.07, Sr1 5 0dBZ min , Sr2 520.03 dBZ min , Sr3 5 21 20.15 dBZ min , Q1 5 0.5, Q2 5 0.2, Q3 5 0.2, Q4 5 0.1, and Yth 5 0.7. Also, the corresponding clusters to each isocluster set are found in Fig. 19. The matching cluster is referenced by its isocluster ID.)

Isocluster 21 ID jwj 2 1 Kf Ke Sr (dBZ min ) hf he hr gk Y Decision 182120.155 0.25 0.13 0.00 0.58 0.21 Nonchaff 210020.020 — — — 0.44 — — 31823 3 0.104 0.00 0.17 0.00 0.12 0.05 Nonchaff 418141620.028 0.78 0.89 0.93 0.77 0.83 Chaff 554420.048 0.80 0.80 0.85 0.53 0.78 Chaff 697420.121 0.78 0.44 0.24 0.78 0.60 Nonchaff 71491220.032 0.64 0.86 0.98 0.33 0.72 Chaff 8 11 4 4 0.001 0.36 0.36 0.00 0.72 0.32 Nonchaff 943020.217 0.75 0.00 0.00 0.63 0.44 Nonchaff aspects of their identification process that can contribute through procedures that overwrite the modified portion of to (work in favor of) the performance outcome of the the data back to the original radar data. These mapping and proposed QC routine. data writing procedures are described in Fig. 21. We note that all data in the background pool category 1) Chaff clusters are always selected among reasonably (A, F , and W ), those that were not subject to the in- sized clusters (small sporadic clusters are exempted c S ference system, are not removed from original radar from being chosen). This could significantly impact the data. performance index, especially if small sporadic clusters CEDR QC simulation results for the studied cases are (always filtered out by the proposed QC method) make shown in Fig. 22. up a large percentage of input image data. 2) Candidate clusters are followed through (over many time frames). Using cluster dissecting tools, the pro- 8. Conclusions files of the vertically dissected area are viewed in This paper outlines the radar QC procedure for the time series. detection and removal of chaff clutter from reflectivity 3) The change in the population of image cells exhibit- data obtained by single-polarization radar measure- ing high reflectivity is noted. ments. Specifically, we considered ground echo removed (CZ) reflectivity data. The proposed QC procedure consists of spatial and temporal image element clustering 7. Chaff echo removal procedure algorithms (for cluster tracking); generic cluster charac- The CEDR QC within the framework of the KMA terization routines describing both static and dynamic weather radar system optimization is required to have both characteristics of clusters; and an inference system pred- original input and quality controlled output radar data in icated on four inputs, one binary output single-layer specific radar data format. This means that the original ra- perceptron NN (with a threshold activation function) that dar data must be mapped to quality controlled output data serves as the decision support mechanism for target

TABLE 6. Normal cluster statistics for KSN 1300 KST 3 Jun 2010 data.

Isocluster Volume Coverage Maximum Mean reﬂectivity ID X (km) Y (km) Z (km) (km3) area (km2) reﬂectivity (dBZ) (dBZ) 1 229.2 167.5 0.90 516 145 23.00 10.34 2 230.9 186.8 0.70 92 107 22.00 10.73 3 296.5 270.3 1.70 1921 1027 48.00 18.94 4 268.2 312.4 4.27 41 330 12 437 43.00 14.21 5 153.9 230.1 1.76 595 310 42.00 20.73 6 322.9 223.2 1.50 322 220 32.00 13.34 7 170.1 141.7 2.87 4092 1478 43.00 21.65 8 386.4 136.6 3.96 932 270 42.00 21.26 9 329.7 64.5 4.25 508 182 33.00 13.11

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FIG. 20. Procedure for the calculation of the performance index (PI) of CEDR QC procedure. cluster identiﬁcation. The proposed method was applied to 116 randomly selected cases, mostly from weekday sample data from 2010 and 2011. The main contributions of this work are as follows: 1) The introduction of a novel amorphous radar object- tracking procedure in real time, based on a spatial and temporal clustering algorithm with respective O(n)

FIG. 21. CEDR QC algorithm featuring chaff clutter removal. TABLE 7. Performance evaluation results for 116 randomly selected cases between 2010 and 2011. Each sample is separated by at least 6 h of time difference, and was collected from various sites that include Oseongsan (KSN), Jindo (JNI), Gangneung (GNG), and O(q2) time complexity that is applied only on Gwangdeoksan (GDK), Myeonbongsan (MYN), Seongsan (SSP), selected radar image elements, resulting in dramatic Gwanaksan (KWK), Gudeoksan (PSN), Gosan (GSN), Baengyeongdo n (BRI). speed improvement ( is the total number of input image data points above threshold reflectivity, q is the Case average number of input clusters above a specific vol- Both chaff Only ume within a single time frame 3D image); and 2) and nonchaff nonchaff Total a demonstration that a cost-effective detection of chaff present present cases clutter from CZ reflectivity data is achievable at an Number of cases 80 36 116 81% accuracy level through time series analysis of re- Performance index (PI) 0.79 0.87 0.81 flectivity image data without resorting to other radar Standard deviation 0.163 0.156 0.165 moment data at the range gate level.

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FIG. 22. Chaff echo removal simulation results. (top) Full echoes with below-threshold reﬂectivity echoes removed. (middle) Identiﬁed chaff echoes using proposed inference system. (bottom) Echoes with below-threshold

reﬂectivity echoes and chaff clutter removed. (a) KSN 1300 KST 3 Jun 2010 (parameter settings: Rth 5 4, h1 5 21 21 21 0.055 km, h2 5 0.270 km, Uth 5 0.07, Sr1 5 0dBZ min , Sr2 520.03 dBZ min , Sr3 520.15 dBZ min , Q1 5 0.5, Q2 5 0.2, Q3 5 0.2, Q4 5 0.1, and Yth 5 0.7). (b) KSN 1300 KST 15 Jun 2010 (parameter settings: Rth 5 4, 21 21 21 h1 5 0.015 km, h2 5 0.270 km, Uth 5 0.02, Sr1 5 0dBZ min , Sr2 520.03 dBZ min , Sr3 520.15 dBZ min , Q1 5 0.4, Q2 5 0.3, Q3 5 0.2, Q4 5 0.1, and Yth 5 0.6).

As an extension of the present work, future research includes the adoption of a new set of feature tracking could focus on incorporating other radar moment data and parameterization schemes related to radar cluster (radial velocity, spectrum width, etc.) into the current characterization and classiﬁcation. CEDR QC inference and training model. However, this also implies a potential increase in both the spatial and Acknowledgments. This research was supported by time complexity of the CEDR QC procedure. Another the Development Project of Radar System Optimization possible approach for improving the detection result grant funded by the Korea Meteorological Administration

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