基于模型阶数选择准则的稳健杂波边缘检测方法

A Robust Clutter Edge Detection Method Based on Model Order Selection Criterion

在雷达目标自适应检测问题当中,辅助数据存在杂波边缘的情况将导致杂波协方差矩阵(CCM)的估计性能出现严重下降,极大地影响目标检测性能。为了解决这一问题,该文提出一种杂波边缘检测方法,能够对辅助数据中杂波边缘数量与位置进行自适应判别。首先,假定辅助数据中存在杂波边缘,采用模型阶数选择算法和最大似然估计方法完成杂波参数估计,其中杂波边缘位置由循环搜索方法得到。之后将杂波参数估计结果应用到检测算法中,通过广义似然比检验方法来判断杂波边缘是否存在。此外为了进一步提升算法在小样本条件下的稳健性,引入CCM的特殊结构作为先验知识,将算法推广至CCM为斜对称、谱对称以及中心对称3种结构的情况。仿真及实测数据均表明该文所提算法能够高效地识别雷达辅助数据中的杂波边缘数量和位置,先验知识的引入更能进一步改善算法在辅助数据量较小时的性能。

In the radar target adaptive detection problem,the presence of clutter edges in the auxiliary data will cause a serious decrease in the estimation performance of the Clutter Covariance Matrix(CCM),which greatly affects the target detection performance.In order to solve this problem,a clutter edge detection method is proposed,which can adaptively discriminate the number and position of clutter edges in auxiliary data.Firstly,assuming the presence of clutter edges in the auxiliary data,the model order selection algorithm and the maximum likelihood estimation method are used to complete the clutter parameter estimation,and the clutter edge position is obtained by the cyclic search method.Then,the clutter parameter estimation results are applied to the detection algorithm,and the existence of clutter edges is determined by the generalized likelihood ratio test method.In addition,in order to further improve the robustness of the algorithm under the condition of small samples,the special structure of CCM is introduced as a priori knowledge,and the algorithm is generalized to the situation where CCM is persymmetry,spectrum symmetry and central-symmetry.Both simulation and measured data show that the proposed algorithm can efficiently identify the number and location of clutter edges in radar auxiliary data,and the introduction of prior knowledge can further improve the performance of the algorithm when the amount of auxiliary data is small.