【作者】 王晓明；

【导师】 李军；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2017， 硕士

【摘要】 杂波抑制是机载雷达目标探测面临的主要问题。空时自适应处理(STAP)算法对杂波进行空域和时域的联合处理,可显著提高机载雷达的杂波抑制能力。实际的杂波环境往往呈现出非均匀、非平稳特性,传统的采用与待检测单元相邻的样本对协方差矩阵进行最大似然估计的STAP算法性能受限,如何准确有效地估计待检测单元的杂波协方差矩阵,成为进一步提升机载雷达目标探测性能的关键问题。本文研究非均匀杂波环境下的空时自适应处理问题,分析了非均匀环境下进行数据样本筛选的必要性,对现有样本筛选算法进行了仿真,并利用实测数据验证了处理算法的实际性能;在此基础上,提出了一种改进样本自适应样本筛选算法,在小系统自由度情况下,具有更优的筛选性能。本文的具体内容概括如下:1.介绍机载雷达杂波回波模型及STAP基本理论。从全空时自由度处理的STAP算法出发,讨论了降维/降秩处理的必要性,介绍了常用降维STAP算法的基本原理,并对STAP处理性能的评估参数进行说明。2.对机载雷达的非均匀杂波环境,进行信号建模与杂波仿真。对比分析了杂波的最小方差功率谱与傅里叶谱。介绍了非均匀环境下的杂波协方差矩阵估计方法及知识辅助STAP(KA-STAP)滤波器的基本原理。3.研究了两种经典的独立同分布(IID)样本筛选算法,即广义内积(GIP)法和基于傅里叶谱相似度(FSPS)的样本筛选算法,讨论了不同的非均匀杂波场景下,两种算法的适用性,并进行了建模仿真和性能对比。结果表明,GIP算法对存在少量离散强杂波单元的非均匀环境有较好的筛选效果,但在存在大量非均匀样本的环境下,算法性能较差;FSPS算法则在强非均匀性杂波环境下呈现出良好的稳健性。利用两种算法对机载雷达实测数据进行了处理,验证了算法的有效性。4.针对在小系统自由度下,已有的FSPS算法在污染样本剔除及相似样本选择环节都存在分辨率不足的问题,提出一种基于稀疏恢复技术的自适应样本筛选算法。该方法利用高精度稀疏恢复信息对参考单元样本进行筛选。相比FSPS算法,该方法在小系统自由度情况下可同时提升污染样本剔除及相似样本选择时的性能。通过计算机仿真验证了该方法的有效性。更多还原

【Abstract】 Clutter and jamming suppression is the primary problem of airborne radar’s target detection.Algorithm of space-time adaptive processing(STAP)can suppress the clutter in both space domain and time domain,which can considerably improve the moving target detection performance of airborne radar.Since heterogeneous clutter environment and the range dependence of short-range are frequently encountered in real applications,the traditional STAP algorithm which uses training samples beside the cell under test(CUT)to estimate covariance matrix do not apply.How to estimate the clutter covariance matrix accurately and efficiently is the crux of further enhancing the moving target detection performance.This work studies the STAP algorithm in heterogeneous interference environments,analyses the necessity of training samples selection in such environments.The existing samples selection algorithms are modeled and simulated with their actual performance presented by measured data.A new adaptive samples selection method is proposed based on the previous algorithm.The new method have a better selection performance when the system’s degrees of freedom(DoF)is small.The main contents comprise the following.1.The second chapter presents the clutter model of airborne radar and the basic theory of STAP algorithm.The necessity of dimension reduction algorithm is discussed after introducing fully STAP algorithm.Give some explanation of famous descending dimension STAP algorithm.Parameters of STAP algorithm performance evaluation are also presented.2.The third chapter gives the simulation of airborne radar’s clutter signals in heterogeneous interference environments.Show the differences between the minimum variance spectrum and Fourier spectrum.Illuminate covariance matrix estimation method in heterogeneous interference environments and the basic principle of knowledge-aided space-time adaptive processing(KA-STAP)filter.3.The Fourth chapter studies two samples selection methods that are inner products algorithm(GIP)and Fourier spectral similarity(FSPS)samples selection algorithm.Discuss the algorithms’ applicability in different heterogeneous interference environments with simulation analysis.The simulation results show the superiority of GIP algorithm when the environment has little heterogeneous samples while show the inferiority of when the environment is strong heterogeneous.FSPS algorithm show the robustness in the high degree of environment’ heterogeneity.Show the effectiveness of both two algorithms by processing measured data.4.The existing training samples selection algorithm based on Fourier Spectral Similarity(FSPS)cannot meet the resolution requirement in both the target signal contaminated samples discarding step and the similar samples selecting step when the system’s degrees of freedom(DoF)is small.To mitigate this problem,this paper proposes a training samples selection algorithm based on sparse recovery technology.Compared with FSPS,the proposed method is able to improve the performance of both the contaminated samples discarding step and the analogous samples selecting step under a small system’s DoF situation.Simulation results verify the effectiveness of the proposed method.更多还原