Active Sonar Research Articles

Continuous active sonar may not fix noise pollution of killer whale habitats

Although quieter, continuous active sonar still interferes too much with killer whale communication.

Auditory masking of tonal and conspecific signals by continuous active sonar, amplitude modulated noise, and Gaussian noise in killer whales (Orcinus orca).

Continuous active sonar is thought to mitigate severe acoustic impacts due to its lower sound pressure level compared to pulsed active sonar typically used by world navies. However, due to its almost continuous duty cycle, continuous active sonar could have a higher potential for auditory masking. Here, we evaluate the auditory masking potential of several noise types including a recording of continuous active sonar, amplitude modulated noise, and Gaussian noise, on signal detection in two killer whales. Signals were either a 1.5 kHz pure tone or a recording of a broadband burst-pulse killer whale call. For the 1.5 kHz tone, all noise types resulted in statistically significant masking, however, there was a release from masking of approximately 13 dB for the amplitude-modulated noise. When the killer whale call was the signal, the whales employed an off-frequency listening strategy where the whales were able to detect frequency components of the signal that did not directly overlap with the noise. However, this strategy was less useful for the continuous active sonar noise due to its broadband harmonic structure. Continuous active sonar has spectral features that considerably overlap with those of killer whale calls, making this type of noise an effective auditory masker.

Behavioural responses of common dolphins to naval sonar.

Despite strong interest in how noise affects marine mammals, little is known for the most abundant and commonly exposed taxa. Social delphinids occur in groups of hundreds of individuals that travel quickly, change behaviour ephemerally and are not amenable to conventional tagging methods, posing challenges in quantifying noise impacts. We integrated drone-based photogrammetry, strategically placed acoustic recorders and broad-scale visual observations to provide complementary measurements of different aspects of behaviour for short- and long-beaked common dolphins. We measured behavioural responses during controlled exposure experiments (CEEs) of military mid-frequency (3-4 kHz) active sonar (MFAS) using simulated and actual Navy sonar sources. We used latent-state Bayesian models to evaluate response probability and persistence in exposure and post-exposure phases. Changes in subgroup movement and aggregation parameters were commonly detected during different phases of MFAS CEEs but not control CEEs. Responses were more evident in short-beaked common dolphins (n = 14 CEEs), and a direct relationship between response probability and received level was observed. Long-beaked common dolphins (n = 20) showed less consistent responses, although contextual differences may have limited which movement responses could be detected. These are the first experimental behavioural response data for these abundant dolphins to directly inform impact assessments for military sonars.

Enhancing Generalization of Active Sonar Classification Using Semisupervised Anomaly Detection With Multisphere for Normal Data

Enhancing Generalization of Active Sonar Classification Using Semisupervised Anomaly Detection With Multisphere for Normal Data

An algorithm for multi-target tracking in low-signal-to-clutter-ratio underwater acoustic scenes

To simplify the computational complexity of the Joint Probabilistic Data Association (JPDA) algorithm for multi-target tracking in low-signal-to-clutter-ratio underwater acoustic scenes, and to solve the problem of unstable tracking caused by poor measurement accuracy of active sonar, a trajectory information-based JPDA optimization algorithm (TJPDA) is designed to meet the real-time and stability requirements of practical engineering. The false alarm rate is reduced by accumulating correlated target trajectories in multiple frames, and the influence factor is introduced to modify the correlation probability and simplify the confirmation matrix splitting process. This effectively solves the problem of information combination explosion and trajectory divergence caused by adjacent target interference in low signal-to-clutter-ratio underwater acoustic scenes. The simulation results show that compared with the JPDA algorithm, the TJPDA algorithm not only improves target-tracking accuracy but also reduces computation time, making it more suitable for practical engineering applications.

Temporal-Feature-Based Classification of Active Sonar Targets in a Deep-Water Environment

Temporal-Feature-Based Classification of Active Sonar Targets in a Deep-Water Environment

Bio-inspired target detection method of active sonar based on multi-ping perception

Abstract The performance of the active sonar may degrade severely in real ocean environments, influenced by strong reverberation and multiple clutters. Inspired by the observation that dolphins always utilize a train of clicks when detecting underwater, we proposed an active target detection method by making full use of the multi-ping echo information which encompasses the differences between targets and reverberation/clutters. The detection performance of the click trains with different parameters were analysed first to give an insight on signal choice. Then we achieved apparent reverberation/clutter suppression result by utilizing the correlations and differences between multi-ping echoes. Further, the relative motion status between the target and the sonar platform could be perceived within multiple echoes, which strengthened the detection ability of weak target. We proposed two weighting strategies for multi-ping co-utilization. Either equal weights or unequal weights based on the different envelopes of dolphin echolocation click trains were exerted on multiple echoes, which can achieve different perception results for the relative target motion status. Both simulation and lake trials were conducted to verify the performance of the proposed method. The proposed method effectively suppress reveberaton and noise background by around 2-3 dB and achieve bionic detection of weak targets compared to traditional active sonar signal processing method.

Cascaded-Filter-Based Reverberation Suppression Method of Short-Pulse Continuous Wave for Active Sonar

Reverberation is the main background interference in active sonar and seriously interferes with the extraction of the target echo. Active sonar systems can use short-pulse continuous wave (CW) signals to reduce the reverberation intensity. However, as the pulse width of the CW signals decreases, the reverberation envelope exhibits a high-frequency oscillating phenomenon. Active sonar often uses the cell average constant false alarm ratio (CA-CFAR) method to process the reverberation, which steadily decays with transmission distance. However, the high-frequency oscillation of the reverberation envelope deteriorates the performance of CA-CFAR, which causes a higher false alarm rate. To tackle this problem, the formation mechanism of the high-frequency oscillation characteristics of the reverberation envelope of the short-pulse-width CW signals is modeled and analyzed, and on this basis, an α filter is designed to suppress the high-frequency oscillation of the reverberation envelope before applying CA-CFAR. The simulation and lake trial results indicate that this method can effectively suppress high-frequency oscillations of the reverberation envelope, as well as exhibit robustness and resistance to reverberation interference.

Shallow sea reverberation suppression based on a range azimuth patch matrix model.

Reverberation is the main background interference for active sonar detection in shallow sea. Reverberation suppression is crucial for enhancing the performance of active sonar. In this paper, a reverberation suppression method based on low-rank sparse decomposition is proposed. First, both the sparseness property of the targets and the non-local self-correlation property of the reverberation are used to construct a range azimuth patch matrix model. The reverberation suppression problem is then transformed into an optimization problem for the recovery of a low-rank sparse matrix. The validity of the proposed method is verified by using the measured data. Results show that, compared with the reverberation pre-whitening and sparse fractional Fourier transform methods, the proposed method significantly improves the reverberation suppression performance and achieves a better detection result when the signal-to-interference ratio is below -2 dB.

An Interactive Transient Model Correction Active Sonar Target Tracking Method

Active sonar can usually only directly measure the distance and bearing information of underwater targets, and cannot directly obtain target velocity, acceleration and other information. Therefore, the amount of information is relatively small, making it difficult to support the construction of complex motion models. At the same time, the motion state of underwater maneuvering targets is changeable. In response to the problem of detecting and tracking underwater moving targets by active sonar, this paper proposes a target transient model correction (TMC) filtering tracking method. Based on the conventional Kalman filter (KF) estimation, residual covariance is used as a signal quantity. When there is a large change in it, a transient filter with constant gain is adopted to filter the measurement value. The filtered output is used to correct the KF gain matrix and the target motion state model, to avoid the problem of increasing or even diverging KF estimation errors caused by changes in process noise. Using this method can solve the problem of maintaining stability and filtering estimation accuracy of active sonar tracking of underwater maneuvering targets with less computational and engineering costs.

Data augmentation based on highlight image models of underwater maneuvering target

With the development of underwater acoustic countermeasure technology, deep learning is applied to recognize echo geometry features of underwater targets, but it faces the problem of sample scarcity. In this paper, we improved the underwater target highlight model, and established the target echo information equation of active sonar. By changing the spatial positions of target and sonar regularly, we performed the highlight image models of underwater maneuvering targets. Taking an underwater vehicle as an example, the model construction process was introduced in detail, and highlight image models of four typical acoustic scale decoys were also established, and five multi-space state highlight image data samples were generated. The eHasNet-5 convolutional classification network was designed, and the network was trained, verified and tested with the generated data. Finally, the experimental data test shows that the target highlight image generation models provide a new data augmentation method for the application of deep learning in active sonar target recognition, and the trained network by generated data has the ability to classify two-dimensional objects.

A coherent integration method of an active sonar for maneuvering turning target detection.

The detection probability of underwater weak targets using active sonar is low, and inter-pulse coherent integration can improve the signal-to-noise ratio of echoes. When a target executes a maneuvering turn, complex range and Doppler frequency migrations occur during the coherent integration time that decrease the coherent integration gain. Most existing integration methods simplify the target motion to a finite-order polynomial model but fail to integrate a maneuvering turning target (MTT) due to model mismatch. Hence, this study proposes an underwater MTT integration method based on the modified Radon-Fourier transform. The proposed method constructs a theoretically accurate motion model for the MTT and a phase compensation function to compensate for the Doppler frequency migration. Furthermore, it yields a well-focused integration peak in the range-velocity and offset angle-turn rate dimensions and accurately estimates the target motion parameters. Moreover, the proposed method is suitable for targets with radial and oblique uniform motions. The effectiveness of the proposed method is demonstrated through simulations and a lake test. The proposed method demonstrates good integration performance, with an integration gain approximately 4-7 dB higher than that of traditional methods when using 30 integration pulses.

An Adaptive Tracking Method for Moving Target in Fluctuating Reverberation Environment

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method.

Common dolphin whistle responses to experimental mid-frequency sonar.

Oceanic delphinids that occur in and around Navy operational areas are regularly exposed to intense military sonar broadcast within the frequency range of their hearing. However, empirically measuring the impact of sonar on the behavior of highly social, free-ranging dolphins is challenging. Additionally, baseline variability or the frequency of vocal state-switching among social oceanic dolphins during undisturbed conditions is lacking, making it difficult to attribute changes in vocal behavior to anthropogenic disturbance. Using a network of drifting acoustic buoys in controlled exposure experiments, we investigated the effects of mid-frequency (3-4 kHz) active sonar (MFAS) on whistle production in short-beaked (Delphinus delphis delphis) and long-beaked common dolphins (Delphinus delphis bairdii) in southern California. Given the complexity of acoustic behavior exhibited by these group-living animals, we conducted our response analysis over varying temporal windows (10 min- 5 s) to describe both longer-term and instantaneous changes in sound production. We found that common dolphins exhibited acute and pronounced changes in whistle rate in the 5 s following exposure to simulated Navy MFAS. This response was sustained throughout sequential MFAS exposures within experiments simulating operational conditions, suggesting that dolphins may not habituate to this disturbance. These results indicate that common dolphins exhibit brief yet clearly detectable acoustic responses to MFAS. They also highlight how variable temporal analysis windows-tuned to key aspects of baseline vocal behavior as well as experimental parameters related to MFAS exposure-enable the detection of behavioral responses. We suggest future work with oceanic delphinids explore baseline vocal rates a-priori and use information on the rate of change in vocal behavior to inform the analysis time window over which behavioral responses are measured.

Hear You Say You: An Efficient Framework for Marine Mammal Sounds’ Classification

Marine mammals and their ecosystem face significant threats from, for example, military active sonar and marine transportation. To mitigate this harm, early detection and classification of marine mammals are essential. While recent efforts have utilized spectrogram analysis and machine learning techniques, there remain challenges in their efficiency. Therefore, we propose a novel knowledge distillation framework, named XCFSMN, for this problem. We construct a teacher model that fuses the features extracted from an X-vector extractor, a DenseNet and Cross-Covariance attended compact Feed-Forward Sequential Memory Network (cFSMN). The teacher model transfers knowledge to a simpler cFSMN model through a temperature-cooling strategy for efficient learning. Compared to multiple convolutional neural network backbones and transformers, the proposed framework achieves state-of-the-art efficiency and performance. The improved model size is approximately 20 times smaller and the inference time can be 10 times shorter without affecting the model’s accuracy.

Performance of an approximate Bayes factor based detector for environmentally informed active sonar

Developed here are a space of approximate Bayes Factor (BF) inference processors for high frequency broadband active sonar with short vertical arrays operating in shallow water ocean waveguides that exploit relatively depth invariant modes of propagation. The relevant information regarding the refractive media, rough surface and volume reverberation are incorporated to build the marginal likelihoods under each of the composite hypotheses of null and alternative. Acoustic scattering from a mobile object of interest under depth uncertainty characterizes the compound alternative hypothesis. Approximations are presented and inferences regarding the presence of the mobile body of interest are determined against a composite null hypothesis of reverberation and ambient acoustic noise. The approximate BF processor is shown to be a time-varying quadratic form of array observations over the beam-delay space. We demonstrate the sub-space processing of depth invariant modes at range and illustrate the BF inferential approach on a few representative waveguides. Performance in classic terms of probability of detection as a function of false alarm rate are presented. [This work supported by the Office of Naval Research.]

Phenomenon of reverberation intensity oscillation observed in shallow-water reverberation experiment

Reverberation has always been a disturbance for active sonar. In the Yellow Sea reverberation experiment conducted in July 2014, the motion of the soliton internal wave was recorded by the temperature sensor attached to the vertical array. The effect of the soliton internal wave on reverberation was investigated from both the temperature chain and the reverberation static experiment. Through the processing of the data in this section, it is found that the soliton internal wave can cause the generation of clutter and the oscillation phenomenon of the average reverberation intensity, and the subsequent research can realize the acoustic telemetry to forecast the internal wave and further improve the performance of the active sonar.

Acoustics at UMass Dartmouth

The University of Massachusetts Dartmouth has a long history spanning 5 decades of graduate courses offerings and research in underwater acoustics, transduction and signal processing leading to M.S. and Ph.D. degrees in Electrical Engineering. Collaborations between Marine Science, Physics, Mechanical, Bioengineering, and Electrical Engineering departments offer many interdisciplinary opportunities in Acoustics. Courses include Fundamentals of Acoustics, Vibrations, Underwater Acoustics, Electroacoustic Transducers, Medical Ultrasonics, Sonar, Digital Signal Processing, Array Processing, Random Signals, Information Theory, Communications, Detection and Estimation. Many unique facilities including an Underwater Acoustic Test Tank, Open-Ocean water access, and Unmanned Underwater Vehicles support both undergraduate and graduate projects. Research focuses include transducers and transduction science, materials characterization, calibration, array and sonar signal processing, animal bioacoustics, communications, detection and estimation, active and passive sonar funded by Office of Naval Research and Industry.

Issues in mid-frequency sound intensity fluctuation on the Washington Shelf

Strong mid-frequency sound intensity fluctuations were found on the Washington shelf during an experiment in the summer of 2022. The Scintillation Index (SI) of integrated sound energy on single channels at 20 km range was near unity for both 3.5 kHz and 6.0 kHz signals. Accompanying oceanographic measurements from moorings and a towed profiling system revealed sound field variability at multiple time and spatial scales caused by linear and nonlinear internal waves, internal tides, and coastal trapped waves. While the 2022 dataset provides valuable information for addressing issues on intensity fluctuation, additional experimental data are needed to understand the impacts on active and passive mid-frequency sonar systems. To that end, a second Washington Shelf experiment is being planned for the summer of 2025. This talk will discuss the planned measurements and the range of issues to be addressed. These include the frequency-dependence of SI over 1–10 kHz, broadband fading, vertical and horizontal coherence, and the impacts of variability on propagation in subsurface ducts. These measurements are important for investigating fundamental oceanographic causes of sound fluctuation as well as for the design of appropriate signal processing methods. (Work supported by the Office of Naval Research.)

Coupled models for designing and predicting performance of active sonar systems

Active sonar systems used to survey and image the seafloor typically transmit a pulse on a projector, receive echoes on an array of hydrophones, and condition and digitize the signals using on-board electronics. The quality of the data produced by these sensors is a function of the transducers, the electronics, and the environment. Simultaneous modeling of all these factors is necessary for accurate prediction of the system performance. Data products from these systems are often uncalibrated because absolute calibration is not required for their applications. However, modeling active sonar systems in their natural units within each domain provides clear benefits such as allowing system limitations to be accurately identified early during the design phase and easing validation of system performance in comparison with experimental data. This presentation will discuss the use of coupled multi-domain models in the design of two active sonar systems: the Sediment Volume Search Sonar and a multibeam echosounder architecture based on sigma-delta conversion. By coupling electro-mechanical transducer models and electrical hardware models with the Point-based Sonar Signal Model (PoSSM), it was possible to identify critical design parameters and sensitivities in the design of these sensors.