Underwater source localization at multiple uniform linear array sonars with heterogeneous sensor position errors

Direct regressions for underwater acoustic source localization in fluctuating oceans

The author describe a new matched-field acoustic source localization technique using reduced-rank signal processing. The problem is posed as a simultaneous detection and parameter estimation in which unknown, deterministic noise will act to obscure the source. They derive a reduced-rank likelihood ratio array processor and demonstrate its ability to detect and localize the signal of interest. The key advantage of this reduced-rank processor is its ability to remove the interference present while retaining the pertinent information necessary for detection and localization. This is accomplished using singular value decompositions of data matrices. >

The current state of the problem of applying holographic interferometry in hydroacoustics is presented. The discussion of holographic signal processing is focused on solving the problem of monitoring the underwater environment, ensuring the detection, resolution, and localization of underwater noise sources under conditions of low signal-to-noise ratio and spatial-temporal disturbances in the oceanic environment. The localization of the sound source is understood as determining the bearing, radial velocity, range, and depth. Single vector-scalar receivers and linear antenna arrays are considered as receiving modules. The results of theoretical studies, numerical modeling, and field experiments are provided, allowing for the assessment of the effectiveness of holographic processing under realistic conditions.

We derive fast wideband algorithms, based on measurements of the acoustic intensity, for determining the bearings of a target using an acoustic vector sensor (AVS) situated in free space or on a reflecting boundary. We also obtain a lower bound on the mean-square angular error (MSAE) of such estimates. We then develop general closed-form weighted least-squares (WLS) and reweighted least-squares algorithms that compute the three-dimensional (3-D) location of a target whose bearing to a number of dispersed locations has been measured. We devise a scheme for adaptively choosing the weights for the WLS routine when measures of accuracy for the bearing estimates, such as the lower bound on the MSAE, are available. In addition, a measure of the potential estimation accuracy of a distributed system is developed based on a two-stage application of the Cramer-Rao bound. These 3-D results are quite independent of how bearing estimates are obtained. Naturally, the two parts of the paper are tied together by examining how well distributed arrays of AVSs located on the ground, seabed, and in free space can determine the 3-D position of a target The results are relevant to the localization of underwater and airborne sources using freely drifting, moored, or ground sensors. Numerical simulations illustrate the effectiveness of our estimators and the new potential performance measure.

In the field of underwater acoustic signal processing, broadband array signal processing is an important research issue, which is more complex than that of narrowband. Broadband passive localization of underwater acoustic sources is investigated in this study. In order to improve the ability to locate multiple radiation sources which are emitted from ships, the orthogonal propagator method (OPM) is proposed to estimate direction of arrival (DOA) for the target. Based on the theory of spatial spectrum estimation, the linear decomposition of the covariance matrix is adopted to form the orthogonal propagator, and the spatial spectrum is constructed according to orthogonality of subspaces. Then, the directions of ship radiation noises are determined by searching the extreme points of the spatial spectrum. For broadband underwater acoustic signals, the non-coherent subspace method is used to average the effective frequency points in bandwidth to reduce noise interference, and OPM algorithm operating in broadband underwater acoustic signal, on arbitrary sonar array, is derived. In order to verify this method, numerical simulations are conducted to test its performance in localization accuracy, beam width and spatial resolution. During the simulations, a uniform linear array consisting of 96 elements is designed. Additionally, comparisons between the proposed method and conventional beamforming technique are conducted. Comparison results show that the OPM method performs better in spatial directivity and spatial resolution.

Underwater Acoustic Source Localization and Sounds Classification in Distributed Measurement Networks

Extension of the operational range of underwater location techniques has been related to the incorporation of reliable acoustic propagation models, that closely predict the behaviour of oceans. In this paper, the performance of ocean tomography and passive localization of underwater acoustic sources is studied, by analyzing the coupling of performance degradations imposed by modeling mismatches on the ability to estimate the position of acoustic sources.

For shallow underwater acoustic sources in deep-sea environments, the received signals in the direct wave zone exhibit distinct multipath arrival structures during large-depth reception, which can be exploited for passive source localization. This paper presents a method for estimating source depth and range through joint matching of arrival angles and time-delay differences between the direct wave and the first-order sea-surface reflected wave. The arrival angles are obtained from deep-sea particle velocity fields based on ray theory, while the time-delay differences are extracted from the autocorrelation function of the received signals. Simulation results show that both arrival angles and time-delay differences can be accurately estimated simultaneously at signal-to-noise ratios (SNRs) above 10 dB, allowing high-precision passive source localization with the proposed method. Under fixed source-receiver geometry, seabed topography and sea-surface roughness significantly alter acoustic propagation paths. Seabed variations affect paths of both direct and bottom-interacted waves, and sea-surface roughness specifically distorts the path of the first-order sea-surface reflected wave, thereby reducing the accuracy of time delay difference estimation in matched field processing. Validated using data from a 2020 deep-sea acoustic propagation experiment in the Western Pacific—where a single vector hydrophone captured signals from a towed source—the method effectively estimated the depth and range of shallow underwater acoustic sources within a 15 km range, with mean relative errors of 5.5% in depth and 6.2% in range. Compared to conventional methods that use only arrival angles or time-delay differences, this joint-matching strategy offers an efficient and robust solution for passive localization of underwater acoustic sources in the deep-sea direct wave zone.

Underwater imaging plays an important role in sonar detection. Compressive sensing can achieve high-precision airborne sound source localization with a small computational effort, but underwater target localization and ranging have not been fully achieved. This study proposes a metamaterial-based compressive sensing imaging method for underwater sound source localization and target imaging. To overcome the poor wave manipulation performance caused by the impedance difference between water and the structure, a strong coupling interaction between the flexible structure and the medium is introduced to reduce reflection and scattering, dramatically improving the acoustic signal transmission amplitude via the resonance mechanism. Analytical and numerical results show that this device can achieve directional irrelevant modulation of incident acoustic waves in different directions. Besides, the multi-information fusion compressive sensing method and dual-sensor joint compressive sensing method can reconstruct the sound source information from the received mixed signals. Experimental results show that the metamaterial-based compressive sensing imaging device realizes the perception and localization of a single sound source underwater in multiple directions. The metamaterial-based hydroacoustic imaging device eliminates the dependence on large-caliber arrays and complex control systems, thus opening the way to establish a sonar system with low cost and high precision.

Effects of sea surface waves and wind-generated bubbles on underwater sound source localization in the Persian Gulf

Information-theoretic measures of acoustic source localization performance provide for performance comparisons that are valid across classes of signal processing algorithms and can be used as performance criteria for the optimization of receiver-array spatial configurations for source localization. This work investigates the use of fundamental information-theoretic quantities, including mutual information of source location and processor output and conditional entropy of source location given processor output, as performance criteria for the optimization of array configurations. Applications of these criteria to the optimization of horizontal and vertical arrays are illustrated in examples of Bayesian localization, conventional beamforming, and matched-field processing of the acoustic field of a time-harmonic source in a range-independent shallow-water waveguide. The optimized array spatial configurations are compared with results obtained using traditional (energy-based) performance measures. [Work supported by ONR.]

A novel sparse representation based multi-source localization method is presented in this work. We envision a wireless network infrastructure containing multiple phase arrays of acoustic sensors. With multiple arrays, direct estimation of a set of source locations is achieved using a new joint sparse representation of array covariance matrices (JSRACM). This representation transforms the source location estimation problem into a spatial sparse signal representation (SSSR) optimization problem. To mitigate the high computation complexity of JSRACM, a novel binary sparse indicative vector (SIV) is introduced to represent the support of joint SSSR of array covariance matrices. As such, the multiple source locations may be estimated by solving an unconstrained optimization problem of the SIV vector using existing FOCUSS-like algorithms. The resulting SIVR-JSRACM algorithm does not require prior information of the number of sources nor initial source location estimates. It promises super-resolution, robustness to noise, and low computing complexity which is independent of the number of sensor phase arrays. Simulation results demonstrate superior performance of the proposed algorithm.

Multipath propagation and reverberation of underwater acoustic signal affect the accuracy of localization estimation based on time difference of arrival (TDOA). To solve the problem, a novel algorithm is proposed to improve the accuracy of localization estimation of underwater acoustic source. Based on the signals received at an array of sensors, a general framework for acoustic source localization using particle filtering is proposed. A generic particle filtering framework was derived. The simulation results demonstrate the superiority of the proposed method. The resulting particle filter is shown to outperform traditional acoustic source localization method.

An auxiliary source-based near field source localization method with sensor position error

Source localization in the context of underwater environment has been recognized as an important but challenging research topic. Conventional methods usually require a receiver array with accurate time synchronization and a large number of elements. The feasibility of accurately localizing an underwater source by a low-cost and small underwater vehicle is greatly limited. Our previous work has successfully applied matched field processing concept to do single-vehicle underwater source localization with informative path planning. However, it requires accurate environmental knowledge of entire search space. In this paper, we focus on improving the robustness of our originally proposed approach. This improvement aims to ensure that our proposed source localization approach performs well even with the presence of environmental uncertainties. Simulation studies show that our robust method is capable of mitigating the effect of environmental mismatch in the MFP model for source localization.