基于粒子滤波的MIMO雷达目标似然比检测方法

针对多输入多输出（MIMO）雷达在非高斯杂波背景、信号参数随机或未知的情况下，难以建立目标统计检测模型的问题，基于粒子滤波方法，根据参数的概率分布函数抽取粒子，将复杂积分运算转化为求和运算求取似然函数，给出了一种MIMO雷达目标似然比检测的通用模型，解决了非高斯杂波条件下无法得到检测统计量的问题．以复SG-Alpha联合稳定分布作为MIMO雷达非高斯杂波分布模型，给出了基于粒子滤波的似然比检测方法．最后通过仿真实验给出了该方法在不同条件下的检测性能，并与传统MIMO雷达目标检测方法进行了比较，结果表明在非高斯杂波背景下该方法的检测性能要明显优于传统方法的．

Multiple input multiple output radar target likelihood-ratio detection using particle filtering

It is not easy to establish multiple input multiple output(MIMO) radar statistical test model in non-Gaussian clutter backgrounds when signal parameter is stochastic or unknown.Based on the particle filtering,the proposed method calculated the likelihood function by transforming the integral operation to the sum operation according to the probability distribution function of unknown parameter.And a general MIMO radar target likelihood-ratio detection model is established.This method is available for different kinds of clutter and it resolves the difficulties that it is unable to establish statistical test model in non-Gaussian clutter backgrounds.Taking SG-Alpha stable distribution as the model of non-Gaussian clutter,a likelihood-ratio detection method based on particle filtering is established.Moreover,simulations show that the detection performances of the proposed method are better than the traditional MIMO radar detection method in non-Gaussian clutter backgrounds.