Improved iterative reweighted L1 norm minimization method for sound source identification

Sparse reconstruction algorithm is one of the main research topics in compressed sensing. To address the shortcomings of existing iteratively reweighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm minimization methods, which exhibit poor performance in low-frequency sound source identification and weak anti-interference capability, this paper proposes an improved iteratively reweighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-norm minimization method. Unlike traditional methods, this method introduces a log-sum penalty function and constructs a surrogate function, transforming the problem into an effective form for solving the source strength distribution vector. Through numerical simulations comparing the two methods under different frequencies and signal-to-noise ratios (SNR), the results demonstrate that the proposed method enhances both the sound source identification accuracy and anti-interference capability of the algorithm, while also being able to adapt to lower frequency ranges.