非均匀非高斯杂波的协方差矩阵估计

Covariance Matrix Estimation of the nonhomogeneous and non-Gaussian Clutter

在雷达目标检测中,杂波的协方差矩阵估计利用了待检测单元附近的杂波数据。本文考虑一种非均匀环境中,非高斯杂波下的杂波协方差矩阵估计问题,即假定待检测单元与参考单元的杂波协方差矩阵之间满足某种统计关系,并假定杂波数据满足复合高斯统计分布模型。在这种场景下,常规的杂波协方差矩阵估计方法会导致信号检测性能的下降。采用共轭先验分布作为非均匀非高斯场景的统计分布模型,利用贝叶斯方法,本文给出了基于Gibbs抽样的杂波协方差最小均方误差估计方法。计算机仿真结果表明,与常规的杂波协方差矩阵估计方法相比较,本文所给出的杂波协方差矩阵的估计算法能够在参考数据较少,累积脉冲个数较少时,非均匀场景中获得较好的检测性能。本文还分析了先验分布模型参数误差对检测性能的影响。

For the problem of the radar targets detection,the clutter data around the cell under test is used for the estimation of the clutter covariance matrix.In this paper,we consider the covariance matrix estimation of compound Gaussian clutter of nonhomogeneous environments,i.e.,the training samples used for adaptation do not share the same covariance matrix as the vector under test, and the clutter can be modeled in terms of a compound Gaussian process.The conventional covariance matrix estimations have poor performance under this environment.In this paper,the conjugate prior distributions of unknown parameters of clutter statistics are used to descript the nonhomogeneous environments,and based on Bayesian framework,the minimum mean square error estimation of clutter covariance matrix is proposed,using Gibbs sampler.The computer simulation is used to validate the method proposed in this paper,and the results show that the method of the covariance matrix estimation proposed in this paper is better than conventional ones/,especially within a small number of training samples and coherent pulses.The impact of the error of parameters of prior distributions on the detection performance is also analyzed in the end of the paper.