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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。
In sparse signal detection problems, two important challenges are how to improve the detection accuracy and reduce the computational complexity. A local likelihood ratio selection (LRSL) method is proposed and used to detect sparse signal segments in one-dimensional noise data. Unlike the general LRS approach, the LRSL method first picks points whose observed values are above a certain threshold and then detects them from within the neighborhood of those points. Due to the sparsity of signal segments, the LRSL approach can significantly reduce computational complexity. In addition, theoretical asymptotic results indicate that the LRSL method can detect weaker signals than LRS. Simulation results show that the proposed method has higher detection accuracy and detection efficiency.