Passive Sonar Signal Processing Research Articles

A Novel Beam-Domain Direction-of-Arrival Tracking Algorithm for an Underwater Target

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach.

Fast Sparse Bayesian Learning Based on Beamformer Power Outputs to Solve Wideband DOA Estimation in Underwater Strong Interference Environment

Wideband direction-of-arrival (DOA) estimation is an important task for passive sonar signal processing. Nowadays, sparse Bayesian learning (SBL) attracts much attention due to its good performance. However, performance degrades in the existence of strong interference. This problem can be solved by combining the beamformer and the SBL. The beamformer is a useful tool to suppress interference. Then, the SBL can easily estimate the DOA of the targets from the beamformer power outputs (BPO). Unfortunately, the latter step needs to compute the matrix inversion frequently, which brings some computational burden to the sonar system. In this paper, the BPO-based SBL is modified. A sequential solution is provided for the parameters in the BPO probabilistic model. In this manner, only one signal precision parameter involved in the probabilistic model is updated in each iteration and the matrix inversion is avoided during the iteration, thus reducing the computational burden. Simulation and experimental results show that the proposed method maintains high estimation precision in the interference environment. At the same time, its computational efficiency is almost three times higher in comparison with state-of-the-art methods.

Passive Tone Detection for Moving Targets Based on Long-Time Coherent Integration

The detection of sinusoidal signals embedded in noise is an important topic in passive sonar signal processing. The performance of existing tone detection techniques is limited by the Doppler frequency shift, which is caused by the relative motion between moving targets and the receiver. In this study, an algorithm based on long-time coherent integration is proposed to achieve robust tonal signal detection under the influence of a Doppler frequency shift. First, the received narrowband signals radiated by moving targets are split into multiple segments through a window-length constraint. The results obtained by applying a discrete Fourier transform (DFT) to the data segments are modeled as a polynomial phase signal in the frequency domain. Then, the polynomial Radon-polynomial Fourier transform (PRPFT) is applied to simultaneously compensate the time-variant frequency drift and phase difference. To avoid the gain loss due to the discretization of the phase difference compensation in PRPFT, a phase compensation factor searching algorithm is proposed. The simulation results show that the proposed algorithm can provide higher frequency resolution and higher coherent integration gain even in the case of a severe Doppler shift. Furthermore, the results of sea trials demonstrate that the proposed method can perfectly achieve coherent integration for signals with a duration of several hundred seconds under different experimental conditions.

A Fast Variational Bayesian Adaptive Extended Kalman Filter for Robust Underwater Direction-of-Arrival Tracking

The underwater direction-of-arrival (DOA) tracking using hydrophone array is an important research subject in passive sonar signal processing. To deal with the unknown measurement noise results from underwater environmental noise, the existing variational Bayesian adaptive extended Kalman filter (VB-AEKF) technique for robust underwater DOA tracking jointly estimates the measurement noise covariance matrix and the bearing angle of a target. The iteration process and the nonlinear measurement model result in significant computational complexity of the VB-AEKF. By deriving computational process which is equivalent to VB-AEKF but more computationally efficient, a fast variational Bayesian adaptive extended Kalman filter (FVB-AEKF) for robust DOA tracking is proposed. The proposed method is tested via Monte Carlo simulations of an underwater DOA tracking scenario and an experiment in the South China Sea in July 2021. The results of the simulations and the experiment verified the accuracy and the computational efficiency of the FVB-AEKF.

Focusing on motion and averaging the diagonal: Lisa Zurk’s many contributions to passive sonar array processing

Lisa Zurk’s numerous contributions to passive sonar signal processing include motion compensation and regularizing sample covariance matrices (SCM) in snapshot deficient environments. While working at MIT Lincoln Labs, Lisa and colleagues compared the performance of several motion compensation methods including mode-based rank reduction and target motion compensation, or focusing, on the Santa Barbara Channel Experiment dataset [Zurk, Lee & Ward, JASA, 2003]. Later at Portland State, Jorge Quijano and Lisa developed methods for regularizing the SCM through Toeplitz averaging, followed by maximum entropy extrapolation of the SCM to additional lags, [Quijano & Zurk, JASA, 2017]. Toeplitz regularization found significant application in developing “augmented covariance matrices” for DOA estimators on sparse arrays. This talk reviews some of Lisa’s contributions to adaptive beamforming and highlights how some of her contributions inspired research in the UMass Dartmouth Signal Processing Group. [Work supported by ONR Code 321US.]

Lisa Zurk’s contributions to striation-based signal processing for active sonar

Strong multipath propagation adds considerable complication to acoustic signal processing in the ocean. When mapped in range-frequency space, the acoustic field exhibits striations, alternating bands of high and low intensity due to constructive and destructive interference between the paths. Nearly forty years ago, Russian scientists showed how this striation pattern could be described by a single scalar parameter, the so-called waveguide invariant that subsequently became a staple of their passive sonar signal processing methods. Lisa Zurk’s contribution was to show how a striation-based approach could be adapted to active sonar processing. Together with her students, she did tank experiments and analysis for monostatic and bistatic configurations. For a horizontal array, she developed a beamformer featuring a linear frequency shift across the array designed to align with high-intensity striations. The result was improved performance in noisy environments. She showed how the waveguide invariant can improve tracking accuracy by providing a constraint on possible tracks. She and her students demonstrated the improved tracking with continuous active sonar data. Lisa Zurk’s innovative work on these topics continues to inspire present day research.

Robust underwater direction-of-arrival tracking with uncertain environmental disturbances using a uniform circular hydrophone array.

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, considering that an unknown underwater environment results in uncertain disturbances to the measurements, robust underwater DOA tracking with regard to uncertain environmental disturbances was studied. Because the uniform circular array (UCA) is free from the port and starboard ambiguity problem, a UCA was used to obtain the measurements for a long-time tracking scenario. First, a kinematic model of an underwater target and a measurement model based on the received signal of the UCA were established. Then, a DOA tracking algorithm was derived based on the extended Kalman filter (EKF), whose performance is significantly affected by the accuracy of the measurement noise covariance matrix (MNCM). Finally, considering that uncertain disturbances carry out unstable measurement noise, the modified Sage-Husa algorithm was used to obtain accurate MNCMs during the process of the derived EKF-based DOA tracking algorithm. Thus, a robust DOA tracking method with uncertain environmental disturbances using a UCA was proposed. The accuracy and reliability of the suggested method was verified via Monte Carlo simulations of a DOA tracking scenario and an experiment in the South China Sea in July 2021.

A Sparse Perspective for Direction-of-Arrival Estimation Under Strong Near-Field Interference Environment.

Direction of arrival (DOA) estimation via sensor array is a crucial component of any passive sonar signal processing technology. In certain practical applications, however, the interested far-field targets are frequently affected by near-field interference, which may result in degradation of DOA estimation. Aiming at the direction estimation problems of far-field targets under strong near-field interference, a unified sparse representation model of far-field and near-field hybrid sources is constructed according to the various correlations in steering vectors between the planar wave and spherical wave in this paper. A high-resolution spatial spectrum reconstruction algorithm based on a sparse Bayesian framework is then exploited to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary, thus ensuring the accurate detection and estimation of far-field targets. An expectation-maximization (EM) algorithm approach is introduced to estimate the number of sources and noise power iteratively, which will reduce the dependence of the algorithm on such prior information. Several state-of-art algorithms are mentioned and discussed (Minimum Variance Distortionless Response (MVDR) method, Multiple Signal Classification (MUSIC) algorithm and conventional beamforming (CBF) algorithm) to compare with the one proposed in this manuscript that achieves higher accuracy of estimation and resolution under low SNR level with limited samples, which is verified by simulation and for the results obtained in an experimental case study.

Source localization based on matrix filter and sparse asymptotic minimum variance

Wideband direction of arrival (DOA) estimation plays an important role in passive sonar signal processing. Recently, sparsity-based DOA estimation method has attracted considerable attention because of its high resolution in the condition of few snapshots and low signal-to-noise ratio. However, the localization accuracy is seriously affected by the interferences. Matrix filter (MF) has been widely used in passive sonar systems as a useful tool to passband the targets-of-interest while attenuating the interferences, but the output of the MF seriously affected subsequent DOA estimation when the power of the interferences after filtering is still stronger than the weak targets. In this paper, a method based on MF and sparse asymptotic minimum variance (SAMV) is given to localize the weak targets in a strong interference environment. The given method improves the ability of SAMV on weak targets localization and achieves high localization accuracy even in the condition that the power of the interferences after filtering remains stronger than the weak targets, which is verified by simulation and experimental results.

Wideband sparse spatial spectrum estimation using matrix filter with nulling in a strong interference environment.

Wideband direction of arrival (DOA) estimation using a sensor array plays a fundamental role in passive sonar signal processing. Although sparsity-based DOA estimation methods can attain high resolution in the condition of few snapshots and low signal-to-noise ratio, the localization accuracy is seriously affected by strong interferences. In this paper, a matrix filter with nulling (MFN) is used to pass weak targets in sector-of-interest (passband) while attenuating the out-of-sector (stopband) interferences by forming deep nulls toward the directions of interferences adaptively. Then, a method based on sparse spectrum fitting (SpSF) and MFN is proposed to localize closely spaced wideband signals in a strong interference environment. In comparison with the minimum variance distortionless response and SpSF, the proposed method achieves higher localization accuracy, which is verified by simulation and experimental results.

Passive and active sonar signal processing methods for port infrastructure protection and harbor security

High frequency passive and active sonar signal processing methods can be used in cluttered shallow water complex acoustic harbor environments for the detection, classification, localization and tracking of asymmetric threats including fast inshore attack craft, divers, underwater vehicles, and sea mines. For fast inshore attack craft, the event duration is short (tens of seconds) which necessitates the automation of the functions of a high frequency high resolution active sonar. The sonar also cues effectors that enable a layered defense capability to counter the threat. For wide area passive undersea surveillance, single hydrophones can be used for multipath passive ranging, a pair of hydrophones for direction finding, and an array of hydrophones for estimation of a complete set of target motion parameters. Also, it will be shown that the measurement of the time delays of the direct path and multipath arrivals at a single pair of hydrophones enables estimation of a complete set of target motion parameters for a surface vessel.

Acoustic detection, localization, and tracking of tactical autonomous aerial and underwater vehicles

By applying single sensor narrowband and multisensor broadband passive acoustic signal processing methods to real microphone data, it is shown that low observable tactical autonomous aerial vehicles can be detected, classified, localized, and tracked by unattended land-based acoustic surveillance systems that exploit a target's acoustic signature. Similarly, the application of passive sonar signal processing methods to real hydrophone data reveals the presence, position and track of an autonomous underwater vehicle by exploiting its radiated noise and its underwater acoustic communication transmissions. A suite of these narrowband and broadband acoustic signal processing methods will be identified and their functional performances quantified.

근접센서를 이용한 수중 표적 정보 추정기법

본 논문은 근접센서를 이용하여 수중 표적의 정보를 추정하는 수동소나 신호처리 기법을 제안한다. 이 기법은 수중 센서망을 구성하는 단일 센서에서 수행되며 계층적 신호처리 구조를 가진다. 이를 통해 추정되는 표적 정보는 표적의 속도, 깊이, CPA 상황에서의 거리와 방위각이며 계층적 신호처리 구조를 가짐으로써 신호처리 기법의 정확도를 향상시킬 수 있다. 제안하는 기법의 성능을 검증하기 위해 모의실험을 수행하였고 이를 통해 최대 탐지거리 내에서 최대 20%의 오차를 가짐을 확인하였다. 또한 해상실험을 통해 실제 해상환경에서 신뢰성을 가지는지 확인하였다.

Waveguide invariance for active sonar.

Active sonar signal processing in shallow water has proven to be a challenging problem due to the strong interaction of sound with the boundaries of the channel and the dependence on typically unknown environmental parameters. This has motivated research on properties of acoustic propagation that are not sensitive to those factors, such as the waveguide invariance. The invariance principle has found application in passive sonar signal processing by relating the frequency content of a broadband source to the range between source and receiver. More recently, experimental evidence has suggested that a similar structure exists for active sonar. This structure provides additional information about the location of a target, and information can be exploited in sonar processing algorithms such as target tracking. Data from several sea experiments have been analyzed to determine the behavior of the active invariance, and tank experiments have been designed to confirm the presence of the range‐frequency structure in signals reflected by a moving target. This presentation provides an overview of the active invariance phenomena and describes the performance of a target tracking formulation that incorporates invariance structure into the state space representation for improved performance.

Efficient Decimation Filter Design for Lofargram Analysis in Passive Sonar Systems

A new class of low-sensitivity decimation filters based on the polyphase decomposition of infinite impulse response (IIR) transfer functions is proposed. The design approach makes use of optimized positioning of poles and zeros so that transfer functions having unequal numerator and denominator orders are obtained. Comparisons with conventional elliptic filters are shown. An illustrative example of application in passive sonar systems using real noisy data is provided. A weighted stopband filter design is presented and its performance in improving lofargram analysis in passive sonar signal processing is verified.

An introduction to time-delay estimation, localization, and tracking in Acoustics

Acoustic source localization and tracking has a long, productive history in both passive and active sonar signal processing. More recently, microphone arrays have been used to localize and track air acoustic noise sources. An overview of basic localization and tracking techniques of acoustic radiating sources and scatters is presented. For a wide range of applications, many ad-hoc passive and active source localization techniques have been investigated. Concepts from the statistical estimation theory, such as maximum likelihood estimators and Cramér-Rao lower bounds (CRLB), are useful benchmarks for evaluation of these practical ad hoc systems. In both air and underwater media, bearing and range estimates are determined by time-delay estimates; hence, properties of the time-delay estimates are of critical importance. In the case the of narrow-band active sonar, localization and tracking is based on time delay, Doppler, and direction of arrival estimation. In the case of wideband sonar and tracking of high-speed air acoustic sources, tracking and range estimates are determined from time-scale dilation estimates. CRLBs for active sonar are determined by signal-to-noise ratio, signal properties of signal ambiguity functions such as effective duration and mean-square bandwidth of the transmitted signal, and array parameters. [This work has been supported by ONR.]

Cramer-Rao lower bounds for sonar broad-band modulation parameters

A role of passive sonar signal processing is the detection and estimation of the parameters associated with amplitude modulated broad-band signals. An example of such signals is propeller noise. Discrete frequency lines occur at the rotational frequency of the propulsion shaft and at the blade frequency. This correspondence provides expressions for the Cramer-Rao lower bounds for the estimates of broad-band signal power, modulation level, modulation frequency, and modulation phase. It is shown that for low broad-band-signal-to-broad-band-noise ratios, the estimates of power and modulation level are uncoupled from the estimates of modulation frequency and phase.

Application of probability modeling and Bayesian inversion to passive sonar signal processing. I. Concepts and formulation

This work considers the benefits of, and methods for the application of probability modeling to passive sonar signal processing. The equal-likelihood probability modeling approach systematically accounts for the combination of data, a priori information, propagation models, array models, and linear and nonlinear models for sources, noise, and environmental descriptors. In Part I, model-consistent, nonstationary, non-Gaussian, equal-likelihood probability models are defined. An optimal nonstationary space-time beamformer and maximum a posteriori probability parameter estimation are derived from equal-likelihood distributions based on autocovariance models employed in conventional matched field processing. Combining information in different frequency bands is seen as an application of a natural data fusion property of stationary Gaussian probability modeling. Part II presents simulation results for the ocean acoustic source localization problem for a shallow water ocean environment under low signal-to-noise conditions. These simulations demonstrate the time evolution of the a posteriori distribution for increased data integration time for single and multiple sources, and illustrate data fusion over a range of frequencies and multiple sensors.

Application of probability modeling and Bayesian inversion to passive sonar signal processing. II. Application to ocean acoustic source localization

Part I of this series considers a probabilistic approach to signal processing using Bayesian inversion. This approach allows incorporation of prior knowledge, fusion over multiple data to improve parameter estimates, and interpretation of data in spite of incomplete knowledge. In Part II this inversion technique is applied to the problem of localizing an acoustic source in a shallow water ocean environment. Simulation results are presented that demonstrate the incorporation of additional data to improve the source localization. Further simulation results demonstrate implicit data fusion over frequency and multiple sensors. A third set of simulations examines the case of two sources and demonstrates isolation of a weak source in the presence of a strong interfering source.

The importance of correlators

A tutorial review of generalized cross correlators and their importance to passive sonar signal processing will be presented. A discussion of the effects bandwidth, and integration time as well as multipath and multitarget interferences will be included. The presentation will emphasize broadband aspect of correlation processing including degradations experienced by narrow‐band components. Fundamental limits on performance will be discussed.