In the scenario of ambient noise, the noise powers received by the pressure and velocity components in an underwater acoustic vector sensor (AVS) array are unequal. This paper proves when using the MUSIC method, this inequality causes virtual sources and thus increases the rank of the signal subspace. This fact dramatically degrades the DOA estimation performance of the MUSIC method. Then, an augmented subspace (AS) MUSIC method is proposed to take account of the virtual sources, by augmenting the number of the virtual sources into the signal subspace. Simulation results demonstrate in the case of a high signal‐to‐noise ratio (SNR), the performance of the AS MUSIC method and the MUSIC method is similar. However, in the case of a low SNR, the AS MUSIC method is superior to the MUSIC method in terms of spatial spectrum, estimation accuracy, and resolution. Experimental results further verify the superiority of the AS MUSIC method over the MUSIC method.

The ambient noise covariance matrix for the array of underwater acoustic vector sensors (AVSs) is not equal to a scaled identity matrix. This fact contradicts the requirement of subspace-based DOA estimation methods such as the conventional MUSIC method, leading to the performance degradation of their DOA estimation. In order to overcome this problem, we explore the real- and imaginary- property of the auto-correlation and cross-correlation of the ambient noise, and propose a MUSIC-based DOA estimation method with asymptotically Ambient Noise Elimination (Named as ANE MUSIC method). In particular, the ANE MUSIC method first transforms the array covariance matrix to a new one of which the noise is concentrated in the real part. Thus, the imaginary part of the transformed covariance matrix eliminates ambient noise. Afterwards, based on the imaginary part of the transformed covariance matrix, the ANE MUSIC method employs a real-valued Singular Value Decomposition (SVD) to complete DOA estimation. The proposed ANE MUSIC method is asymptotically independent of the ambient noise. Therefore, it is robust to the ambient noise in the case of limited snapshots. In addition, since its involved spectral searching is over only half of the total angular field-of-view with a real-valued noise subspace, the ANE MUSIC method reduces the computational complexity by about 75% in terms of spectral searching, as compared to the conventional MUSIC method which utilizes the complex-valued Eigenvalue Decomposition(EVD) and a spectral searching over the total angular field-of-view. It is noted that the proposed ANE MUSIC method does not require to know the prior noise covariance matrix, which is different from the existing prewhitening solution. Simulation results demonstrate the ANE MUSIC method performs significantly better than the other methods, especially in the case of low signal-to-noise ratios (SNRs). Moreover, it gains a certain robustness against the sensor gain-phase errors. Experimental results verify the practical effectiveness of the ANE MUSIC method, based on the real data collected by an array of two AVSs in the anechoic water tank and the real data collected by an uniformly circular array of eight AVSs in the Songhua Lake in Jilin, China.

To resolve the radiated-noise sources of submerged submarines in shallow water, a new source localization method was developed based on generalized inverse beamforming (GIB). We chose a slender cylindrical shell to simulate the submarine, and a horizontal acoustic vector sensor array (AVSA) was applied to conduct source localization. Generally, the location of the horizontal array is close to the water bottom during measurements, and the acoustic reflection by the water bottom is the main factor degrading the performance of the source localization algorithms. To reduce or eliminate the water-bottom reflection, we proposed a new AVSA processing method, which is called combined signal processing (CSP). The proposed CSP method can form a unilateral directivity pattern that suppresses the image source signals without distorting the physical source signals. Subsequently, we introduced two iterative regularization matrices to the GIB algorithm, which contained a priori source information, and repeatedly penalized the iterative solution to improve the localization results. Numerical simulations and experiments were conducted to verify the proposed source localization method, the results of which suggest that it outperforms conventional source localization methods in terms of the dynamic range and spatial resolution of source maps, as well as the localization accuracy.

Aiming at the engineering limitations of traditional ship vibration online monitoring and noise prediction methods, this paper proposes a method for online monitoring and underwater radiation noise prediction of cabin structures’ vibration and noise under hybrid excitation of sound and force. The method first constructs the condition test model; based on OTPA technology, the “acoustic-vibration” transfer function between sound and vibration monitoring points in the cabin and the “acoustic/vibration-acoustic” transmission network inside and outside the cabin structure are obtained. Secondly, based on the “acoustic-vibration” transfer function, the online vibration and sound monitoring data are decoupled and processed to obtain the modified vibration and sound monitoring data. Finally, the near-field radiation noise on the conformal hologram surface outside the cabin is predicted based on the “acoustic/vibration-acoustic” transmission network, and the far-field radiation noise of the cabin structure is predicted by the wave superposition method. In this paper, the contribution law of external radiation noise and the coupling characteristics of the monitoring information under the hybrid excitation of sound and force are analyzed theoretically, and the decoupling method of coupling information is also studied. This method makes up for the problem of missing underwater radiation noise caused by sound excitation in traditional vibration monitoring, and it can effectively improve the prediction accuracy of underwater radiation noise. The effectiveness of the method is further verified by the tank model experiments.

With the rapid development of the theory and algorithms for sparse recovery in finite dimension, compressive sensing (CS) has become an exciting field that has attracted considerable attention in signal processing, such as sub-Nyquist sampling systems, sound imaging and reconstruction, wavelet denoising, compressive sensor networks, and so on. Moreover, the broad applicability of CS framework has already inspired some notable investigation in the context of array processing. The problem of acoustic source identification can be investigated from a limited number of measurements delivered by a microphone array as a basis pursuit problem, which has been developed in the context of compressive sensing, and the CS beamforming can be proved to be better than the conventional beamforming even in its near-field focusing version based on spherical waves. Focused beamforming is a typical method used to localize the position of acoustic sound sources in the near field of the measurement array, and can be a jointly reconstructed source powers and positions. Many super-resolution focused beamforming approaches have been developed to overcome the Rayleigh resolution limit of conventional focused beamforming. Especially, turning to the compressive sensing (CS) framework, we are able to exploit the inherent sparsity of the underlying signal in space domains to achieve super-resolution for the focused beamforming even in a noisy and coherent environment with few snapshots.Prior research has established CS as a valuable tool for array signal processing, but it is mainly from a theoretical point of view, and its application to underwater acoustic sources localization has been developed only for very limited scenarios. In this paper, we present an underwater noise sound source near-field localization method based on a sparse representation of vector sensor array measurements. By utilizing the sparsity approach, the new localization methods can jointly reconstruct source powers and positions, and enforce sparsity by imposing penalties, based on the l1-norm, to improve the integrated performance. By comparing with other source localization methods, such as the conventional focused beamforming, MVDR focused beamforming, and the maximum likelihood focused beamforming, the performance of compressive focused beamforming and the typical focused beamforming using pressure or vector sensor array is analyzed in detail, especially under noisy conditions, and coherent sources. Simulation and experimental results demonstrate that this new approach has a number of advantages over other source localization techniques, e.g. increased resolution, improved robustness to noise, limitations in data quantity and correlation of the sources, as well as lower levels of background interference. It is feasible to apply the proposed approach for effectively localizing and identifying underwater noise sound sources.