摘要
This paper presents a novel near-field source localization method based on the time-frequency sparse model. Firstly, the method converts the time domain data of array output into time-frequency domain by time-frequency transform; then constructs sparse localization model by utilizing the specially selected time-frequency points, and finally the greedy algorithms are chosen to solve the sparse problem to localize the source. When the coherent sources exist, we propose an additional iterative selection procedure to improve the estimation performance. The proposed method is suitable for uncorrelated and coherent sources, moreover, the improved estimation accuracy and the robustness to low signal to noise ratio （SNR） are achieved. Simulations results verify the efficiency of the proposed algorithm
This paper presents a novel near-field source localization method based on the time-frequency sparse model. Firstly, the method converts the time domain data of array output into time-frequency domain by time-frequency transform; then constructs sparse localization model by utilizing the specially selected time-frequency points, and finally the greedy algorithms are chosen to solve the sparse problem to localize the source. When the coherent sources exist, we propose an additional iterative selection procedure to improve the estimation performance. The proposed method is suitable for uncorrelated and coherent sources, moreover, the improved estimation accuracy and the robustness to low signal to noise ratio （SNR） are achieved. Simulations results verify the efficiency of the proposed algorithm
基金
supported by the National Natural Science Foundation of China(60901060)
