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Examination of Statistics and Modulation of Underwater Acoustic Ship Signatures
Mark Trevorrow DRDC – Atlantic Research Centre
Terms of Release: This document is approved for public release.
Defence Research and Development Canada Scientific Report DRDC-RDDC-2021-R027 March 2021
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IMPORTANT INFORMATIVE STATEMENTS
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© Her Majesty the Queen in Right of Canada (Department of National Defence), 2021 © Sa Majesté la Reine en droit du Canada (Ministère de la Défense nationale), 2021
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Abstract
This Scientific Report examines ship underwater acoustic signature amplitude statistics and statistical distributions. This explores the hypothesis that ship signatures exhibit amplitude fluctuations that are different from Rayleigh-distributed ambient ocean noise. Signature measurements from two different ships conducting a variety of manoeuvres are examined, focusing on those conditions where propeller cavitation and broadband signal modulation occur, specifically during maximum speed runs, accelerations, and turning manoeuvres. A key difference for a ship signature is the amplitude modulation generated by propeller cavitation, and this is found to be associated with super-Rayleigh signal characteristics. The use of new cyclostationary processing techniques is used to estimate propeller shaft and blade rate modulation. Under conditions of stronger propeller modulation, time-series statistics scintillation index and skewness show values significantly in excess of Rayleigh-distributed values. Ship signature amplitude probability density functions were found to be better matched by a K-distribution model with small shape factor, indicating increased presence of higher-amplitude signal components.
Significance to Defence and Security
The ship signature statistics examined in this study quantify the acoustic texture of the signal, describing features that might be used intuitively by experienced acoustic operators to identify a ship signature and determine its operating state (e.g., steady cruising vs. manoeuvring). It is believed that this result may be useful in advanced sonar detectors and classifiers, including use in modern artificial intelligence algorithms, against surface ships and other vehicles employing propellers.
Conventional active and passive sonar processors generally use detection thresholds based on the assumption of Rayleigh statistics for the background noise. When ship noise contributes to the background noise for active and passive sonars, this super-Rayleigh behaviour may generate increased false alarms, or require an increase in sonar detection thresholds thus reducing potential target detection ranges.
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Résumé
Le présent rapport scientifique porte sur les distributions statistiques et les statistiques d’amplitude de la signature acoustique sous-marine des navires. On examine l’hypothèse selon laquelle la signature des navires présente des fluctuations d’amplitude différentes du bruit océanique ambiant réparti selon la loi de Rayleigh. On étudie les mesures de la signature de deux navires distincts exécutant diverses manœuvres en s’attardant surtout aux conditions dans lesquelles on observe une cavitation des hélices et une modulation des signaux à large bande, en particulier lors de déplacements à vitesse maximale, d’accélérations et de virages. L’une des principales différences dans la signature des navires est la modulation d’amplitude produite par la cavitation des hélices, modulation qui s’avère être associée aux caractéristiques des signaux en régime super-Rayleigh. On utilise de nouvelles techniques de traitement cyclostationnaire pour déterminer la modulation de l’arbre porte-hélice et de la fréquence des pales. En cas de forte modulation de l’hélice, l’indice de scintillation et l’asymétrie de statistiques provenant de séries chronologiques affichent des valeurs nettement supérieures à celles obtenues selon la distribution de Rayleigh. Ainsi, on a constaté que les fonctions de densité de probabilités de l’amplitude de la signature des navires correspondaient davantage à un modèle de distribution K avec un faible coefficient de forme, ce qui indique la présence accrue de composantes de signaux de plus grande amplitude.
Importance pour la défense et la sécurité
Les statistiques sur la signature des navires examinées dans la présente étude permettent de quantifier la texture acoustique des signaux en décrivant des caractéristiques que des opérateurs acoustiques chevronnés pourraient utiliser de manière intuitive pour identifier la signature d’un navire et déterminer son état de marche (p. ex., régime de croisière stable par rapport aux manœuvres). On croit que ces résultats pourraient être utiles pour les récepteurs sonar et les classificateurs plus perfectionnés. On pourrait les utiliser notamment dans les algorithmes d’intelligence artificielle modernes, pour les navires de surface et autres véhicules à hélice.
Les processeurs de sonars actifs et passifs classiques utilisent généralement des seuils de détection fondés sur l’hypothèse des statistiques de Rayleigh sur le bruit de fond. Si le bruit des navires contribue au bruit de fond des sonars actifs et passifs, ce régime super-Rayleigh peut entraîner une augmentation des fausses alarmes ou bien nécessiter un seuil accru de détection sonar, réduisant ainsi la portée de détection de cibles potentielles.
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Table of Contents
Abstract ...... i Significance to Defence and Security ...... i Résumé ...... ii Importance pour la défense et la sécurité ...... ii Table of Contents ...... iii List of Figures ...... v List of Tables ...... viii 1 Introduction ...... 1 2 Review of Ship Signature Characteristics ...... 3 2.1 Ship Signature Spectra ...... 3 2.2 Propeller Modulation Effects ...... 5 2.2.1 DEMON Processing ...... 5 2.2.2 Cyclic Modulation Coherence (CMC) ...... 6 2.3 Ship Signature Statistical Models ...... 6 2.3.1 Time-series Statistics ...... 6 2.3.2 Probability Density Function (PDF) Models ...... 7 2.3.3 PDF Generation and Fitting ...... 8 2.4 Numerical Simulations ...... 8 2.4.1 Spectral Shaping and PDF ...... 9 2.4.2 Time-series Statistics ...... 12 2.4.3 Ship Signature Modulation ...... 13 3 Instrumentation and Sea-Trials ...... 16 3.1 Broadband Underwater Recording Buoys ...... 16 3.2 Spectral Processing ...... 17 3.3 Lloyd’s Mirror Effects ...... 18 3.4 Test Ships ...... 19 3.5 Ship Manoeuvre Types ...... 20 3.6 Sea-trial Locations ...... 21 4 CCGS VECTOR Sea-Trials, April 2005 ...... 22 4.1 Straight-Line Runs ...... 22 4.1.1 Example Run 1302 Straight Pass at 11 Knots ...... 22 4.1.1.1 Spectral Levels and Lloyd’s Mirror ...... 23 4.1.1.2 Signature PDF and Statistics ...... 24 4.1.1.3 Propeller Modulation ...... 27 4.1.2 SSL and Propeller Rate Variation with Speed ...... 29 4.1.3 Variability in PDF and Statistics ...... 30 4.2 Turning Runs ...... 31
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4.2.1 Example Run 1404: 180 Starboard Turn at 11.3 Knot In-run ...... 31 4.2.1.1 Spectral Levels through Turns ...... 33 4.2.1.2 Signature PDF and Statistics ...... 34 4.2.1.3 Propeller Modulation ...... 37 4.2.2 Example Run 1308: 110 Port Turn at 11.3 Knot In-run ...... 38 5 CFAV QUEST Sea-Trials, Sept. 2005 ...... 42 5.1 Straight-Line Constant-Speed Runs ...... 42 5.1.1 Spectral Source Levels ...... 42 5.1.2 Signature PDF and Statistics ...... 43 5.1.3 Propeller Modulation ...... 44 5.2 Straight-Line Accelerating Runs ...... 45 5.2.1 Spectral Source Levels ...... 46 5.2.2 Signature PDF and Statistics ...... 47 5.2.3 Propeller Modulation ...... 48 5.3 Turning Runs ...... 48 5.3.1 Spectral Source Levels ...... 50 5.3.2 Signature PDF and Statistics ...... 51 5.3.3 Propeller Modulation ...... 53 6 Summary Discussions ...... 55 6.1 Recommendations for Further Work ...... 58 References ...... 59 List of Symbols/Abbreviations/Acronyms/Initialisms ...... 61
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List of Figures
Figure 1: Comparison of empirical ship signature models, compared to example ship SSL for CCGS VECTOR at speed of 11 knots...... 4 Figure 2: Comparison of signal power spectra vs. bandwidth and with ship signature shaping. .. 9 Figure 3: Comparison of simulated BB noise signal PDF vs. LP bandwidth, with best-fit Rayleigh and K-distributions...... 10 Figure 4: Comparison of simulated ship signature PDF between broadband, ship spectral shaping, and single-propeller shaft and blade rate modulation. Rayleigh model is fit to ship signature with no modulation. K-distribution model is fit to ship signature with modulation...... 11 Figure 5: Comparison of simulated ship signature PDF including dual-propeller shaft and blade rate modulation. Rayleigh model is fit to ship signature with no modulation. K-distribution model is fit to ship signature with modulation...... 12 Figure 6: Ship signature cyclic modulation coherence (CMC) based on 3 s simulated time-series with 10 kHz LP filtering and inverse-frequency squared spectral shaping. Simulation uses 4 Hz shaft and 12 Hz blade rates, with added 1500 Hz NB tonal...... 14 Figure 7: Ship signature ICMC, based on simulated data in Figure 6 integrated over 1–8 kHz bandwidth...... 14 Figure 8: Comparison of simulated ship signature DEMON spectra at 3 s (1024-pt FFT) and 10 s (4096-pt FFT) data record length...... 15 Figure 9: (left) Photograph of a Broadband Underwater Recording Buoy (BURB), with (right) detail on the hydrophone and attachment of lead weight...... 16 Figure 10: Photograph of CCGS VECTOR conducting a run past two BURBS, April 13, 2005. . 22 Figure 11: Plot of ship speed (from GPS) and horizontal range to BURBs for VECTOR Run 1302. 23 Figure 12: SSL (dB re Pa2/Hz at 1 m) spectra averaged over 8 s at CPA for all BURBs in VECTOR Run 1302, compared to empirical relation due to Ross (Equation (2)) at ship speed of 11.0 knots. Hydrophone depths (upper) 5 m and (lower) 15 m...... 24 Figure 13: Comparison of data and best-fit model PDF for VECTOR Run 1302 BURB2 for an 8 s period at CPA...... 25 Figure 14: Data PDF vs. time for VECTOR Run 1302, BURB2, channel 2. Top plot shows best fit K-distribution shape parameter. Vertical dashed line is CPA...... 26 Figure 15: Skewness vs. scintillation index (1-s blocks) for VECTOR Run 1302, BURB 2 for the period ±30 s from CPA. Dashed lines show Rayleigh values...... 27 Figure 16: Scintillation index vs. K-distribution shape factor for VECTOR Run 1302, BURB 2 for the period ±30 s from CPA. Linear fit shown...... 27 Figure 17: Contour plot of CMC frequency vs. modulation for VECTOR Run 1302, BURB4 channel 2 (15 m depth) calculated over 3 s at CPA (17:35:55 UT)...... 28
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Figure 18: ICMC vs. modulation frequency for VECTOR Run 1302, BURB4 at CPA. .... 29 Figure 19: DEMON spectrum from VECTOR Run 1302 BURB4, computed over 3 s at CPA. . 29 Figure 20: Propeller shaft rate (inferred from ICMC analysis) vs. speed for CCGS VECTOR from multiple straight runs...... 30 Figure 21: Plan view of VECTOR Run 1404. Events A–D described in text...... 32 Figure 22: Plot of ship speed and turn-rate (relative to ground) and horizontal range to BURBs vs. time for VECTOR Run 1404. Events A–D described in text...... 32 Figure 23: SSL (dB re Pa2/Hz at 1 m) spectra averaged over 8 s at CPA for all four BURBs in VECTOR Run 1404, compared to empirical relation due to Ross (Equation (2)) for ship speed of 11.3 knots. Hydrophone depths (upper) 5 m and (lower) 15 m...... 33 Figure 24: Data PDF vs. time for VECTOR Run 1404, BURB2, channel 2. Top plot shows best fit K-distribution shape parameter. Vertical dashed line is CPA and arrows denote events A to C discussed in text...... 34 Figure 25: Comparison of data and best-fit model PDF for VECTOR Run 1404 for an 8 s period at CPA. (left) BURB2 at event A + 5 s, (right) BURB1 at event B + 5 s...... 35 Figure 26: Time variation of scintillation index and skewness for VECTOR Run 1404, BURB2. CPA at 51 s. Arrows denote events A to C discussed in text...... 36 Figure 27: Skewness vs. scintillation index (1-s blocks) for VECTOR Run 1404, BURB 2 for the period 120 s period covering start of turn. Dashed lines show Rayleigh values. Red lines show hypothesized bi-linear relationship...... 37 Figure 28: ICMC modulation frequency vs. time for VECTOR Run 1404 BURB3 channel 2. .. 37 Figure 29: Plot of ship speed and turn-rate (from GPS) and horizontal range to BURB 1 vs. time for VECTOR Run 1308, a 110 turning run at 11.3 knots in-run. Events A–D described in text...... 38 Figure 30: Data PDF vs. time for VECTOR Run 1308, BURB1, channel 2. Top plot shows best fit K-distribution shape parameter. Vertical dashed line is CPA and arrows denote events A to C discussed in text...... 39 Figure 31: Comparison of data and best-fit model PDF for VECTOR Run 1308, BURB 1, for an 8 s period near the point of maximum turn rate...... 40 Figure 32: Time variation of scintillation index and skewness for VECTOR Run 1308, BURB1. Vertical dashed line shows CPA at 180 s. Arrows denote events A to C discussed in text...... 41 Figure 33: SSL (dB re Pa2/Hz at 1 m) spectra averaged over 8 s at CPA for B3 and B4 for QUEST Run 09, compared to empirical relation due to Ross (Equation (2)) at ship speed of 12.0 knots. Hydrophone depth was 20 m...... 43 Figure 34: SSL (dB re Pa2/Hz at 1 m) spectra averaged over 8 s at CPA for B3 and B4 for QUEST Run 12, compared to empirical relation due to Ross (Equation (2)) at maximum ship speed of 13.7 knots. Hydrophone depth was 20 m...... 43
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Figure 35: Comparison of data and best-fit model PDF for QUEST Run 12 (straight pass at 13.7 knots) for an 8 s period at CPA. (left) B3, (right) B4...... 44 Figure 36: ICMC vs. modulation frequency for QUEST Run 12, B3 and B4 at CPA...... 45 Figure 37: Plot of run parameters for QUEST Run 15, a straight acceleration from 6 to 13 knots. (upper) ship speed and horizontal range to B3 and B4 vs. time; (lower) propeller shaft rate and advance ratio. Events A–C described in text...... 46 Figure 38: SSL (dB re Pa2/Hz at 1 m) spectra averaged over 8 s at 3 times for B3 and B4 during QUEST Run 15, a straight acceleration pass. Spectra are compared to empirical relation due to Ross (Equation (2)) at ship speed of 14.0 knots. Hydrophone depth was 20 m. 47 Figure 39: Data PDF for QUEST Run 15, straight accelerating run, compared to best-fit K-distributions for events A, B, C (as discussed in text). Rayleigh curve best-fit to event A data...... 47 Figure 40: ICMC vs. modulation frequency for QUEST rn 15, at events A, B, C. Arrows denote identified blade rate modulation...... 48 Figure 41: Plan view of QUEST Run 21, a 180 port turn at maximum in-run speed. Events A–D described in text...... 49 Figure 42: Time-series of run parameters for QUEST Run 21, a 180 port turn at maximum in-run speed. (upper) ship speed and horizontal range to B4 vs. time; (lower) propeller shaft rate and advance ratio. Events A–D described in text...... 50 Figure 43: SSL (dB re Pa2/Hz at 1 m) spectra averaged over 8 s at 5 times through QUEST Run 21, a 180 turning manoeuvre. Spectra are compared to empirical relation due to Ross (Equation (2)) at ship speed of 13.6 knots. Hydrophone depth was 20 m. Events A–D described in text...... 51 Figure 44: Data PDF vs. time for QUEST Run 21, B4, channel 2. Top plot shows best fit K-distribution shape parameter. Arrows denote events A to D discussed in text. ... 52 Figure 45: Time variation of scintillation index and skewness for QUEST Run 21, B4. Events A to D discussed in text...... 53 Figure 46: ICMC modulation frequency vs. time (6 s averages) for QUEST Run 21, B4 channel 2. Events A to D discussed in text...... 53
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List of Tables
Table 1: Comparison of scintillation index and skewness statistics between different simulated noise and ship signature time-series, based on 10-s time-series...... 13 Table 2: Ship physical characteristics...... 20 Table 3: Summary of time-series statistics and K-distribution shape parameter averaged over 8 s at CPA for each BURB for Run 1302 (straight-line at 11 knots). Rayleigh values included in last row for reference...... 26
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1 Introduction
Moving ships are an important source of underwater noise. Ship noise is a prominent contributor to the background noise in the ocean at very low frequencies [1–3]. This noise, herein denoted the ship’s acoustic signature, can also be used to detect and classify the ship type, and in some cases identify an individual ship. Ship signatures can also impose significant constraints on the operation of low and medium frequency sonars operated from the ship, or by nearby consort ships, particularly when these ships are operated at higher speeds.
A ship acoustic signature is generally the sum of propeller generated sound, sound generated by breaking waves around the ship hull, and machinery sounds, the latter being transmitted through the ship’s hull. Thus, a ship signature contains both narrowband (NB) tonals and broadband (BB) components, generally extending from of order 1 Hz up to 100 kHz. Broadband ship signature spectra are generally dominated by low-frequency components, exhibiting an inverse-frequency-squared dependence above approximately 100 Hz. The strength of a ship’s acoustic signature increases strongly with ship speed [2, 4–10], particularly when the ship is operated at speeds above a propeller cavitation inception speed (CIS). The CIS is typically near one-half the maximum ship speed, depending on propeller and hull design. Another subtle feature of ship acoustic signatures is that the broadband propeller sound in the 1–10 kHz band can be modulated at the propeller shaft rate and blade passage rate [11]. These features of the ship signature are all known to change with ship speed and manoeuvring state.
The ship signature characteristics examined in this study are both the signal amplitude time-series statistics and probability density functions (PDF). These statistics quantify the acoustic texture of the ship signature, describing features that might be described qualitatively as “spikiness” or “choppiness.” Such qualitative information is often used intuitively by experienced acoustic operators to identify a ship signature and determine is operating state (e.g., steady cruising vs. manoeuvring). As a first approximation, underwater noise signals usually exhibit amplitude PDF that are Rayleigh distributed [12]. However, several previous studies have produced evidence for non-Rayleigh and non-linear characteristics of ambient noise containing ship signatures [12–14]. Where the background noise is non-Rayleigh, increases must be made in sonar detection thresholds. As will be examined below, broadband ship signatures can often exhibit signals that are super-Rayleigh, with an increased proportion of high-amplitude signal components. This will affect the performance of conventional automated detectors that assume Rayleigh signal statistics, but may also provide features for ship signature detection and classification.
The overall goal of this study is to examine ship signature statistics and modulation under a controlled set of ship operating conditions, including aggressive manoeuvres.1 Short-range, broadband acoustic recordings from two different ships are examined. The data were recorded during separate sea-trials in April and Sept. 2005 against the CCGS VECTOR and CFAV QUEST, respectively. The VECTOR was a small (40 m length, 520 t) oceanographic ship with a single, three-bladed propeller. The QUEST was a medium-size (76 m, 2130 t) naval auxiliary with twin, five-bladed propellers. In addition to the propulsion differences, the QUEST was acoustically quieted by design, through vibration-isolation of key machinery and use of highly-skewed-blade propellers. These ship signatures are examined here with
1 This work was conducted under Defence Research and Development Canada (DRDC) Project ATRIUM.
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particular emphasis on conditions that exhibit super-Rayleigh characteristics, such as maximum speed and manoeuvring.
The following frequency band definitions will be utilized in this work: very low frequency (VLF) 10–100 Hz, low frequency (LF) 100–2000 Hz, medium frequency (MF) 2.0–10 kHz, and high frequency (HF) 10–100 kHz.
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2 Review of Ship Signature Characteristics
This section describes relevant ship signature characteristics, including propeller modulation and the statistical models used to examine the sea-trial data.
In all of the following, the ship signature data is assumed to be recorded in data blocks that are long compared to the variability of interest. The data used herein were recorded in 1-second blocks at 40,000 samples per second with 16-bit resolution. Signals are continuous across successive blocks. The raw data acquisition utilized an anti-aliasing filter at 18 kHz. From this base time-series, various types of filtering and other signal processing can then be applied. This analysis utilizes the instantaneous amplitude, A(t), of the signature time-series, X(t), which is computed as the absolute amplitude of the so-called analytic function, i.e.,