Acoustic localization in ocean reverberation via matrix completion with sensor failure

Xu, Liya; Liao, Bin; Zhang, Hao; Xiao, Peng; Huang, Jianjun

doi:10.1016/j.apacoust.2020.107681

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…Aiming at acoustic imaging after long reverberation time, a general cross-correlation classification algorithm was proposed by Sun et al [13] to improve robustness to reverberation environmental changes. Meanwhile, based on the principle of matrix low-rank characteristics, Xu et al [14] reported an improved method, and based on dithering technology, Hao et al [15] proposed a GCC-IWF algorithm for underwater reverberation environment. All of them contributed to improving the accuracy of acoustic imaging in a reverberant field.…”

Section: Introductionmentioning

confidence: 99%

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Guo¹,

Chen²,

Xia³

et al. 2023

J. vibroeng.

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Most of the existing acoustic imaging studies in reverberant field ignore the influence of signal-to-noise ratio. As a result, commonly used beamforming algorithms in reverberant backgrounds have poor imaging accuracy for low signal-to-noise ratio sound sources. In response to that problem, an improved adaptive beamforming algorithm called SC-DAMAS is put forward in this paper. The algorithm replaces the free-field Green's function with the impulse response function, making the algorithm more suitable for acoustic imaging of low signal-to-noise ratio in a reverberant environment. Besides, the comparative simulation results with the conventional beamforming method and orthogonal matching pursuit algorithm-based DAMAS, as well as sound source acoustic imaging experiments are carried out to analyze its effectiveness. It is indicated that, in the reverberation field, the SC-DAMAS has no obvious sidelobes and achieves higher positioning accuracy for acoustic imaging of low signal-to-noise ratio sound source than the abovementioned counterparts, and its imaging test result is consistent with the actual situation, which verifies the effectiveness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Guo¹,

Chen²,

Xia³

et al. 2023

J. vibroeng.

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“…To achieve robust DOA estimation with sensor failure, matrix completion methods are developed to recover a complete signal matrix for the impaired array [23][24][25]. In particular, the principles of recursive least squares [23], nuclear-norm-based convex relaxation [26], and reweighted 2,1 -norm [24] can be incorporated with a low-rank regularization for signal matrix completion [27], leading to an effective DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure

Li,

Cheng,

Zheng

et al. 2023

Applied Sciences

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Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation in complicated environments. Although existing matrix completion methods can successfully recover the damaged signals of an impaired sensor array, they cannot preserve the multi-way signal characteristics as the dimension of arrays expands. In this paper, we propose a structural-missing tensor completion algorithm for robust DOA estimation with uniform rectangular array (URA), which exhibits a high robustness to non-ideal sensor failure conditions. Specifically, the signals received at the impaired URA are represented as a three-dimensional incomplete tensor, which contains whole fibers or slices of missing elements. Due to this structural-missing pattern, the conventional low-rank tensor completion becomes ineffective. To resolve this issue, a spatio-temporal dimension augmentation method is developed to transform the structural-missing tensor signal into a six-dimensional Hankel tensor with dispersed missing elements. The augmented Hankel tensor can then be completed with a low-rank regularization by solving a Hankel tensor nuclear norm minimization problem. As such, the inverse Hankelization on the completed Hankel tensor recovers the tensor signal of an unimpaired URA. Accordingly, a completed covariance tensor can be derived and decomposed for robust DOA estimation. Simulation results verify the effectiveness of the proposed algorithm.

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“…In [17], a unitary transformation is applied to the block Toeplitz matrix of received signals, and a fast approximate algorithm is devised for the approximation of the reverberation low-rank subspace. For the localization problem of receiver failure in reverberant environments, a matrix completion algorithm that relies on the low-rank characteristic is developed for the Hankel matrices of received signals [18]. The non-negative matrix factorization (NMF) algorithm is utilized to calculate the low-rank structure based on the non-negative multiplier criterion and has been demonstrated to be effective for underwater blind-separation cases of linear frequency modulation (LFM) signals [19,20].…”

Section: Introductionmentioning

confidence: 99%

Application of a Randomized Algorithm for Extracting a Shallow Low-Rank Structure in Low-Frequency Reverberation

Pang

Gao

2023

Remote Sensing

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The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwater acoustic signal processing. To address this issue, an improved Go-SOR decomposition method is proposed based on the subspace-orbit-randomized singular value decomposition (SOR-SVD). This method successfully extracts the low-rank structure with a certain striation pattern. The results demonstrate that the proposed algorithm outperforms both the original Go algorithm and the current state-of-the-art (SOTA) algorithm in terms of the definition index of the low-rank structure and computational efficiency. Based on the monostatic reverberation theory of the normal mode, it is established that the low-rank structure is consistent with the low-frequency reverberation interference striation. This study examines the interference characteristics of the low-rank structure in the experimental sea area and suggests that the interferences of the fifth and seventh modes mainly control the low-rank structure. The findings of this study can be applied to seafloor exploration, reverberation waveguide invariant (RWI) extraction, and data-driven reverberation suppression methods.

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Acoustic localization in ocean reverberation via matrix completion with sensor failure

Cited by 9 publications

References 39 publications

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure

Application of a Randomized Algorithm for Extracting a Shallow Low-Rank Structure in Low-Frequency Reverberation

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