An Enhanced Method Based on LSTM and Regularized K-SVD for Source Number Estimation

Abstract

Abstract In complex environments with limited snapshots, most source number estimation algorithms encounter significant challenges. To address this issue, an enhancement method is proposed involving the integration of an optimized LSTM model with the regularized K-SVD algorithm.Initially, the optimization model is proposed based on the predictability of the LSTM sequence network. This model utilizes limited data to predict additional snapshots, thereby enhancing the covariance matrix of the signal. Subsequently, a sparse reconstruction algorithm based on the regularized K-SVD method is applied to effectively filter out noise, improving signal quality. The combination of the enhancement method with source number estimation techniques further boosts the performance of traditional algorithms. Experimental analysis confirms the superiority of the proposed enhancement method in colored noise environments. Simultaneously, in experiments with a limited number of snapshots, the proposed method significantly enhances estimation performance.