Non-reconstruction Compressive Detection of Random Signal using Maximum Likelihood Criterion and its Analysis
Non-reconstruction Compressive Detection of Random Signal using Maximum Likelihood Criterion and its Analysis
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发表时间：
2013
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通讯作者：
Zhu Yong-gang
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作者：
Zhu Yong-gang
压缩采样理论能够有效地保持原始信号的结构和信息，因此可以直接对采样点进行处理，而不需要对原始信号进行重构，从而解决了原始信号的检测问题。然而，实际信号多为随机信号，本文首先建立了基于压缩采样无恢复信号检测模型，然后详细推导了随机信号的最大似然检测算法，最后分析了该检测算法的性能，理论分析和仿真表明，在给定虚警概率的情况下，检测概率随压缩比的减小呈对数级数增加，并且在保持很低虚警率的同时，可以获得很高的检测率，最后，验证了宽带信号检测的适用性。
Compressive sampling theory can effectively maintain structures and information of the original signal,so detection tasks of the original signal could be solved by directly processing the samples without reconstructing the original signal.The existing signal detection theory based on CS is directed at the deterministic signal;however,most practice signal is random signal.This paper firstly set up a model of signal detection based on compressive sampling without signal recovery,and then derives the maximum likelihood detection algorithm of random signal in detail,and analysis the performance of this detection algorithm lastly.Theoretical analysis and simulation show that detection probability is increased logarithmic progression with the decrease of the compression ratio for given false alarm probability,in addition,it can achieve very high detection rates while simultaneously keeping the false alarm rate very low.Lastly,the applicability of broadband signal detection is verified.