基于局部LRS方法的稀疏信号片段检测

在稀疏信号的检测问题中,两个重要的挑战是如何提高检测精确度和降低计算复杂度。提出局部似然比选择法(LRSL)并用于检测一维噪声数据中的稀疏信号片段。与一般的似然比选择法(LRS)不同, LRSL方法首先选出观测值大于某个阈值的点,然后再从这些点的邻域内进行检测。 由于信号片段的稀疏性,LRSL方法能够显著地降低计算复杂度。另外,理论渐近结果表明,与LRS相比,LRSL方法能检测到更加微弱的信号。 仿真结果表明所提出的方法具有更高的检测精度和检测效率。

Detecting sparse signal segments by local LRS method

Two important challenges in detecting sparse signals are how to improve the detection accuracy and reduce the computational complexity. The local likelihood ratio selection(LRSL) procedure was proposed to detect and identify sparse signal segments in one-dimensional noise data. Different from LRS procedure which directly choose candidate intervals from all intervals, the LRSL procedure only considers neighborhoods of those points whose observed data greater than some threshold. Because of the sparsity of the signals, the proposed procedure can greatly reduce the computational complexity. On the other hand, asymptotic results demonstrate that the LRSL procedure can detect weaker signals. The simulation results indicate that the proposed procedure has high detection accuracy and computational efficiency.