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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]