基于马尔可夫链的MIMO雷达信号融合检测方法

MIMO radar signal fusion detection method based on Markov chain

MIMO(Multiple-Input Multiple-Output)雷达结合多信号技术和阵列技术的优点,利用空间分集对抗目标RCS闪烁,完成目标检测与跟踪.针对多站雷达信号检测,提出了基于马尔可夫链的非相参积累分布式融合检测方法.首先分析了恒虚警率检测的阈值计算原理,推导出多站数据融合后的二次检测阈值.为改善运用贝叶斯和纽曼-皮尔逊准则检测时运算量大的缺点,提出基于马尔可夫链法的算法.该方法对各站回波数据进行结构化处理,根据不同时刻状态间的统计联系,导入马尔可夫过程,计算出状态转移概率矩阵,逐帧搜索确定目标点迹.仿真实验表明,该算法能够在不提高虚警概率的条件下,明显地降低检测阈值,可以有效提高系统检测性能.

The MIMO radar system consists of a transmit array with widely spaced elements such that each element views a different aspect of the target .The array at the receiver is a conventional array used for direction finding . MIMO radars combine both the advantages of multiple signals technology and the array technology ,so as to decrease the influence of target RCS （radar cross‐section）scintillation owing to the space diversity when detecting and tracking targets .For target detection of MIMO radars ,we put forward a distributed fusion detection method by non‐coherent accumulation based on the Markov chain .Firstly ,we analyze the calculation principle for the threshold of constant false alarm rate （CFAR）detection ,then deduce secondary detection threshold after multiple station data fusion .A fusion detection method based on Markov chain is leveraged toreduce the calculation burden of Bayesian and Newman‐Pearson detection ,.The proposed method structuralizes the echo data and introduces Markov process to calculate the state transition probability matrix according to the state statistical relationship between the different time ,then searches target position by calculating the echo data in sequence .The simulation results show that the proposed algorithm can distinctly improve the detection rate without increasing the false alarm probability .Thus the system detection performance can be effectively improved .