CAJ | 학술논문

基于多任务贝叶斯压缩感知(BCS)理论，该文提出一种使用Laplace先验的目标到达角(DOA)估计算法。该算法利用阵元输出为观测值，将DOA估计转化为Laplace先验约束下的BCS求解稀疏信号问题，使用Laplace先验获得比传统BCS更好的稀疏性。该算法不需要信源个数的先验信息和进行特征值分解，能够适应相干信源场景，仿真结果表明该算法具有比传统BCS方法和经典MUSIC算法更好的DOA估计性能。
기우다임무패협사압축감지(BCS)이론，해문제출일충사용Laplace선험적목표도체각(DOA)고계산법。해산법이용진원수출위관측치，장DOA고계전화위Laplace선험약속하적BCS구해희소신호문제，사용Laplace선험획득비전통BCS경호적희소성。해산법불수요신원개수적선험신식화진행특정치분해，능구괄응상간신원장경，방진결과표명해산법구유비전통BCS방법화경전MUSIC산법경호적DOA고계성능。
Based on the multi-task Bayes Compressive Sensing (BCS), a Direction-Of-Arrival (DOA) estimation strategy using Laplace prior is proposed. The DOA estimation is formulated as the reconstruction of sparse signal constrained by the Laplace prior through the BCS framework. The outputs of array sensors are directly employed as the observations, and the exploiting of Laplace prior leads to better spare property than the conventional BCS method. The proposed method needs not the prior information of the number of sources, needs not the eigenvalue decomposition and can work in the coherent signal scenario. The numerical experiments show that the proposed method has the better performance than the conventional BCS and MUSIC algorithm on the DOA estimation.