Direction finding based on iterative adaptive approach utilizing weighted $$\ell _2$$-norm penalty for acoustic vector sensor array

Wang, Weidong; Tan, Weijie; Shi, Wentao; Zhang, Qunfei; Li, Hui

doi:10.1007/s11045-021-00797-6

Cited by 2 publications

(1 citation statement)

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“…[23] proposed a weighted power compensation sparse iterative algorithm via AVSA, which used the current solution to compensate for the next iteration, and the accuracy was effectively improved. In addition, by compensating for the signal with l 2 -norm to improve the sparsity of signals in the spatial domain, the weighted l 2 -norm based IAA was proposed in [24] to provide accurate DOA. However, these sparse iterative algorithms are based on the assumption of Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

Infinite Weighted p-Norm Sparse Iterative DOA Estimation via Acoustic Vector Sensor Array under Impulsive Noise

Liu,

Zhang,

Wang

et al. 2023

JMSE

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Recently, many direction-of-arrival (DOA) estimation techniques based on sparse representation have been proposed. However, these techniques often suffer from performance degradation issues in the presence of impulsive noise. This paper aims to overcome this challenge in conventional sparse-based techniques on an acoustic vector sensor array (AVSA). Firstly, to remove high outliers from the array output data, the output information of the AVSA is weighted by using the infinite norm. To further suppress outliers, a p-order cost function is formulated by extending the Frobenius norm to lower order, and then the expression of the signal power is quantified. Lastly, the DOA is approximated on the signal power by a spectral peak search mechanism. DOA estimation results based on Monte Carlo simulations validate the accuracy and robustness of the proposed techniques herein compared to the current, available methods in the context of intense impulsive noise, low generalized signal–to–noise ratio (GSNR), and a smaller number of snapshots.

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Section: Introductionmentioning

confidence: 99%