Abstract
Conventional beamforming (CBF) is widely used in underwater acoustic applications due to its simplicity and robustness. Under certain circumstances, incoherent noise is the main disturbance for hydrophone arrays and can lead to a serious decline in the signal power estimation accuracy and signal detection ability of CBF. Since incoherent noise contamination is concentrated along the diagonal of the covariance matrix, we propose to improve the performance of CBF by reducing the diagonal as much as possible to suppress the incoherent noise until the output spatial spectrum becomes sparsest. Mathematically, the denoising problem is convex; hence, it can be solved with guaranteed efficiency and convergence properties. The proposed denoising algorithm is named the sparsity-optimization-based diagonal denoising (SO-DD) algorithm, and its capability is investigated and compared with the recently developed positive-semidefinite-constrained diagonal denoising (PSC-DD) algorithmvia simulation and experiments. The results suggest that both SO-DD and PSC-DD work well under ideal conditions where noise is perfectly incoherent, while SO-DD performs more reliably when noise is partially coherent due to limited sampling and the existence of coherent noise component in practice.
Highlights
Compared to other beamforming techniques, conventional beamforming (CBF) has considerable advantages in terms of robustness and computational cost [2], and it has found wide applications in underwater acoustics [3]–[5]
Based on the sparsity of the spatial spectrum vector (SSV), the sparsity-optimization-based diagonal denoising (SO-diagonal denoising (DD)) algorithm is proposed based on the following idea: remove as much diagonal contamination as possible to reduce the effects of noise until the sparsity of the SSV reaches a maximum, which in turn provides a more accurate estimate of the signal power and better detection ability
To investigate the CBF performance improvement caused by the DD algorithms, we define two parameters, i.e., the relative estimation error (REE) and the mainlobe-to-sidelobe integration ratio (MSIR), to evaluate the signal power estimation accuracy and the signal detection ability of CBF, respectively
Summary
Compared to other beamforming techniques, conventional beamforming (CBF) has considerable advantages in terms of robustness and computational cost [2], and it has found wide applications in underwater acoustics [3]–[5]. PSC-DD works well with perfectly incoherent noise, but its performance degrades seriously if noise is partially coherent due to the limited sampling and the existence of coherent noise component From another perspective, Xia [15] proposed an iterative least-squares DD (ILS-DD) algorithm, which estimates the noise power on the array elements and removes the noise contamination from the diagonal. Xia [15] proposed an iterative least-squares DD (ILS-DD) algorithm, which estimates the noise power on the array elements and removes the noise contamination from the diagonal This algorithm outperforms the PSC-DD algorithm in cases of partially coherent noise, but it requires prior knowledge of the exact number of signals, which is usually difficult to obtain in low-input SNR situations.
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