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To address the challenges of large‐aperture sonar systems passive localisation, this paper proposes the application of sparse Bayesian learning (SBL) for passive target localisation in the wideband beam domain. The proposed algorithm aims to overcome the issues of massive computational requirements for two‐dimensional SBL scanning and increased localisation errors due to interference energy leakage. The wideband beam domain SBL focusing localisation algorithm is developed by constructing an azimuth‐range two‐dimensional transformation matrix to preprocess array data, which effectively reduces the computational load of SBL processing while suppressing strong interference energy leakage in passive sonar operating environments, thus improving the range resolution and parameter estimation accuracy of focusing localisation. Simulation and sea trial data analyses demonstrate the feasibility of the proposed algorithm, with results indicating its superior performance compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Conventional active SONAR systems often use beamformers and matched filters separately to extract bearing and range information from the received signal and offer a straightforward way of creating a two‐dimensional map of the environment. In SONAR systems the minimum‐variance‐distortionless‐response beamformer (MVDR beamformer) is a commonly used type of beamformer, which will reconstruct the receive signal from a certain direction optimally. In terms of detecting the transmit signal, the most used method is the conventional matched filter. Both algorithms are simple to implement and perform well under various noise scenarios. The proposed method combines the beamformer and matched filter by introducing an extended channel model that allows the derivation of a multichannel Wiener filter to solve for the unknown reflection coefficients of the complete two‐dimensional environment. This results in adaptively calculated filter weights that will drastically improve the performance compared to a separate MVDR beamformer and matched filter. In addition, a parameter is introduced with which one can arbitrarily adjust the focus between angular and temporal resolution depending on the application. After the derivation, the performance is demonstrated with simulations and measurements. [ABSTRACT FROM AUTHOR]

The performance of Doppler velocity logs (DVLs) in terms of velocity estimate error is directly linked to the geometry of the beam and the pulse transmitted. Beyond a specific transmitted bandwidth, the phase‐shift beamformer can introduce significant errors in velocity estimation. To delineate the operating mechanism of phase‐shift errors within a phased array of acoustic DVLs, the correlation between bottom echo and velocity distribution, in conjunction with the power‐weighted function, was initially examined predicated on spectral estimation theory. Subsequently, numerical and analytical models of the Gaussian‐shaped Doppler spectrum were formulated. The models are employed to evaluate the velocity estimation inaccuracies attributed to phase shifts in extant DVLs, and the comparative results with field experiments corroborate the model's efficacy in forecasting errors. The theoretical findings evaluate the performance limitations of the current phased array transducer design and provide insights for developing new designs. Pool experimental results show that this design effectively reduces the velocity estimation error caused by phase shift under static conditions and in the presence of Doppler frequencies to a level of almost complete elimination of the error compared to conventional configurations. [ABSTRACT FROM AUTHOR]

Underwater acoustic target recognition (UATR) technology based on deep learning and automatic encoding has become an important research direction in the underwater acoustic field in recent years. However, the existing methods do not have favourable self‐adaptability for different data because of the complex and changeable underwater environment, which easily leads to an unsatisfactory recognition effect. The concept of contrastive learning is introduced into UATR and a model named Contrastive Coding for UATR (CCU) is proposed. Based on the unsupervised contrastive learning framework, the model has been modified for the underwater acoustic field. Thus, the CCU can generate adaptable automatic features according to different data. The experimental test shows that the model is superior to other automatic encoding models and has achieved excellent recognition performance on different underwater acoustic datasets. [ABSTRACT FROM AUTHOR]

A real‐aperture high‐resolution imaging method utilising a multiple‐input multiple‐output (MIMO) sonar with two‐dimensional (2‐D) deconvolution processing is proposed. Via derivation, the authors show that the conventional beamformer (CBF) output of the MIMO sonar can be approximately expressed as the 2‐D convolution between the original angular and range distributions of scatterers and a 2‐D point spread function (PSF). Hence, to improve the imaging performance degraded by the convolution effect, the 2‐D Richardson‐Lucy (R‐L) algorithm is used to deconvolve the CBF output of the MIMO sonar. Specifically, the authors give the iteration formula of the 2‐D R‐L algorithm attached to the MIMO sonar, and take the absolute values of the CBF outputs of the virtual array as the deconvolution inputs. The analytical expression of the 2‐D PSF is also given, which is designed as the angular‐ and range‐domain (amplitude) responses of a far‐field ideal scatterer located in the normal direction of the virtual array. Meanwhile, the authors point out that the mismatch made by the approximation may degrade the imaging performance, and suggest that a small number of iterations in the 2‐D R‐L algorithm can be used to alleviate the mismatch problem. Via numerical simulations and a tank experiment, the authors show that the proposed method can simultaneously increase the angular and range resolutions and suppress the sidelobes, when compared to the existing MIMO sonar imaging method. [ABSTRACT FROM AUTHOR]

Traditional chirp scaling algorithm is often developed based on the quadratic approximation of point target reference spectrum, which neglects the higher‐order coupling between range and azimuth dimensions. Besides, the scaling operation is conducted based on quadratic term of reference range, and the residual quadratic error increases with target range far away from reference range. The analysis of phase error shows that both errors are noticeable under the case of wide‐bandwidth signal, and the imaging performance would be seriously distorted. To solve this problem, the higher‐order coupling and quadratic error are compensated by exploiting sub‐block processing method in the range dimension when the chirp scaling algorithm is exploited to reconstruct targets. Compared to traditional methods, the presented method significantly improves the focussing performance based on the simulation results. The processing of real data further shows that the performance of presented method is superior to that of traditional method. [ABSTRACT FROM AUTHOR]

In recent years, significant effort has been allocated to research on multiple‐input–multiple‐output (MIMO) sound navigation and ranging (SONAR) and RADAR systems. Most work has been conducted on the general theoretical functionality of such systems. Less effort has been applied to considerations of the real‐time MIMO capability, although this is an important factor for the application of these new algorithms in real SONAR systems. To account for this, the following work focusses, after introducing the used methodology and revisiting the general MIMO idea and considered system, on more effective permutations of the involved algorithms in the reduction of floating‐point operations. In this context, the general necessity of the algorithms utilized is shown. Furthermore, it is proven that the reduction in computational load does not affect the performance of the system. In addition, the main algorithmic parts of MIMO systems can be exchanged almost arbitrarily under given restrictions without changing the result. Therefore, the performance differences in floating‐point operations are depicted to give an estimate of the achievable degree of complexity reduction. The results for the investigated systems and algorithms are obtained by applying a system simulation of a simple underwater channel. The obtained results were also verified using a real MIMO SONAR system operating in real time. [ABSTRACT FROM AUTHOR]

Sonar images are valuable in exploring underwater environmental information. As these images are generally limited by the viewing angle, sonar image mosaicking becomes an important research topic. By combining several frames consecutively acquired while the underwater vehicle is manoeuvring, an image with a wider view can be obtained. This study presents a fast sonar image mosaicking approach consisting of denoising, feature extraction, initial matching, splicing, and optimisation. Based on the Euclidean distance between initially matched points and dip angle of connection line, poorly matched feature point pairs are removed to avoid false matching. This way, the success rate of image mosaicking and quality of the resulting mosaicking is effectively improved. [ABSTRACT FROM AUTHOR]

Human echolocation is a technique used by small group of people (normally blind persons) to observe their surrounding via emitting an active signal (usually tongue click) and analyse the return echo. This skill allows them to navigate safely by 'seeing through sound'. Remarkable performances upon echolocating have attracted scholars' attention from multidiscipline field including psychophysical and engineering. Study of waveform diversity has reported the signal is wideband, consisting of multiple frequency components with exponential decay factor that probably made up the entire unique signal. As such, wideband perspective is considered during ambiguity function analysis using wide-band ambiguity function method. The waveform analysis continues and reported optimum autocorrelation function is achieved by adopting a bio-inspired technique, which incorporated gammatone-filter processing. It will be interesting to synthesise the waveform using gammatone-filter approaches prior to relay it into ambiguity function process for analysis. Therefore, it is worth to explore its output results in this paper, which hold new findings that may help to understand how these humans achieve an optimum performance upon echolocating including ambiguity function characteristic. Thus, the new knowledge explored in this paper could be beneficial in emerging concept for new development in radar and sonar system application in near future. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a teach‐and‐repeat path‐following method for an autonomous underwater vehicle (AUV) navigating long distances in environments where external navigation aides are denied. This method utilizes sonar images to construct a series of reference views along a path, stored as a topological map. The AUV can then renavigate along this path, either to return to the start location or to repeat the route. Utilizing unique assumptions about the sonar image‐generation process, this system exhibits robust image‐matching capabilities, providing observations to a discrete Bayesian filter that maintains an estimate of progress along the path. Image‐matching also provides an estimate of offset from the path, allowing the AUV to correct its heading and effectively close the gap. Over a series of field trials, this system demonstrated online control of an AUV in the ocean environment of Holyrood Arm, Newfoundland and Labrador, Canada. The system was implemented on an International Submarine Engineering Ltd. Explorer AUV and performed multiple path completions over both a 1 and 5 km track. These trials illustrated an AUV operating in a fully autonomous mode, in which navigation was driven solely by sensor feedback and adaptive control. Path‐following performance was as desired, with the AUV maintaining close offset to the path. [ABSTRACT FROM AUTHOR]

The time and energetic costs of behavioral responses to incidental and experimental sonar exposures, as well as control stimuli, were quantified using hidden state analysis of time series of acoustic and movement data recorded by tags ( DTAG) attached to 12 sperm whales ( Physeter macrocephalus) using suction cups. Behavioral state transition modeling showed that tagged whales switched to a non-foraging, non-resting state during both experimental transmissions of low-frequency active sonar from an approaching vessel ( LFAS; 1-2 kHz, source level 214 dB re 1 μPa m, four tag records) and playbacks of potential predator (killer whale, Orcinus orca) sounds broadcast at naturally occurring sound levels as a positive control from a drifting boat (five tag records). Time spent in foraging states and the probability of prey capture attempts were reduced during these two types of exposures with little change in overall locomotion activity, suggesting an effect on energy intake with no immediate compensation. Whales switched to the active non-foraging state over received sound pressure levels of 131-165 dB re 1 μPa during LFAS exposure. In contrast, no changes in foraging behavior were detected in response to experimental negative controls (no-sonar ship approach or noise control playback) or to experimental medium-frequency active sonar exposures ( MFAS; 6-7 kHz, source level 199 re 1 μPa m, received sound pressure level [ SPL] = 73-158 dB re 1 μPa). Similarly, there was no reduction in foraging effort for three whales exposed to incidental, unidentified 4.7-5.1 kHz sonar signals received at lower levels ( SPL = 89-133 dB re 1 μPa). These results demonstrate that similar to predation risk, exposure to sonar can affect functional behaviors, and indicate that increased perception of risk with higher source level or lower frequency may modulate how sperm whales respond to anthropogenic sound. [ABSTRACT FROM AUTHOR]

The article analyzing how long-finned pilot whales (Globicephala melas) respond to naval sonar signals. It examines whether they changed their calls to vocally match the sonar signals used during the experiments. It is said that these whales respond to naval sonar by increasing their rate of vocalization while they were huddling in close ranks.

Sonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military, sonar signals are used in lieu of visuals to navigate underwater and/or locate enemy submarines in proximity. In particular, classification algorithm in data mining has been applied in sonar signal recognition for recognizing the type of surfaces from which the sonar waves are bounced. Classification algorithms in traditional data mining approach offer fair accuracy by training a classification model with the full dataset, in batches. It is well known that sonar signals are continuous and they are collected as data streams. Although the earlier classification algorithms are effective in traditional batch training, it may not be practical for incremental classifier learning. Since sonar signal data streams can amount to infinity, the data preprocessing time must be kept to a minimum to fulfill the need for high speed. This paper presents an alternative data mining strategy suitable for the progressive purging of noisy data via fast conflict analysis from the data stream without the need to learn from the whole dataset at one time. Simulation experiments are conducted and superior results are observed in supporting the efficacy of the methodology. [ABSTRACT FROM AUTHOR]