An Improved Underwater Acoustic Positioning Algorithm Based on Dithering Technology

Hao, Xueyuan; Zhang, Xianhe; He, Jun; Yan, Xiaohong

doi:10.2112/si99-012.1

Cited by 1 publication

(1 citation statement)

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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%