摘要
该文提出一种基于压缩感知(Compressive Sensing,CS)的恒虚警率(Constant False Alarm Rate,CFAR)目标检测算法,首先分析了目标在距离单元上具有稀疏特性,并构造了目标回波的稀疏字典,设计特定的测量矩阵以及基于CS的CFAR检测结构,然后实现了对回波信号的压缩测量和CFAR检测,无需对回波信号重构。该文提出的算法具有很好的降噪性能并提高了检测效率,可以对低信噪比、低信杂比信号成功检测。仿真结果表明:当信噪比为-14 d B,信杂比为-10 d B时,该算法与传统匹配滤波检测算法相比,减少了一半数据运算量,性能明显优于压缩匹配滤波检测算法。
A new Constant False Alarm Rate （CFAR） target detection algorithm is proposed based on Compressive Sensing （CS）. Firstly, the spaxsity of target in the distance dimension is analyzed and the sparse dictionary is constructed for the echo signal. Secondly, a certain measurement matrix and CFAR detection structure axe designed based on CS. The proposed detector can detect sparse signals directly with high accuracy without any signal reconstruction. The proposed algorithm has a good noise reduction performance, which can detect low SNR and low Signal-to-Interference Ratio （SIR） signals successfully. Finally, computer simulation results verify that when SNR is equal to -14 dB and SIR is equal to -10 dB, the proposed detector can reduce the half measurements via compared with classical Matched Filter （MF） algorithm. What＇s more, the performance of the proposed detector is better than CS MF algorithm.
出处
《电子与信息学报》
EI
CSCD
北大核心
2017年第12期2899-2904,共6页
Journal of Electronics & Information Technology
基金
国家自然科学基金委员会-中国工程物理研究院NSAF联合基金(U1530126)~~
关键词
目标检测
恒虚警率
压缩感知
测量矩阵
Target detection
CFAR
Compressive Sensing （CS）
Measurement matrix
