One-dimensional DOA estimation method based on the improved weighted L1-SVD

This paper proposes an improved one-dimensional direction estimation method (DOA) based on weighted L1 norm singular Value Decomposition (SVD). Traditional DOA estimation methods usually rely on array signal processing technology to infer the location of the signal source by analyzing the phase difference of the received signal. However, the performance of traditional methods in noisy environments and multipath propagation conditions is often unsatisfactory. To this end, in this paper, by introducing the optimization strategy of the weighted L1 norm, the estimation ability of sparse signals is enhanced. While reducing the influence of noise, the estimation accuracy is improved. In this method, we first constructed a weighted L1 norm optimization model based on array signals, and combined with SVD for feature decomposition to extract useful DOA information from it. Compared with the traditional L2 norm minimization method, the weighted L1 norm is more suitable for scenarios with sparse signals and can effectively suppress the influence of noise and multipath effects. The experimental results show that the proposed method demonstrates better performance under different noise levels and array geometric conditions. Especially in the low signal-to-noise ratio (SNR) environment, it can effectively improve the accuracy and robustness of DOA estimation.