本文選題：空時(shí)自適應(yīng)處理　切入點(diǎn)：協(xié)方差矩陣估計(jì)　出處：《電子科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：雜波抑制是機(jī)載雷達(dá)目標(biāo)探測(cè)面臨的主要問(wèn)題�？諘r(shí)自適應(yīng)處理(STAP)算法對(duì)雜波進(jìn)行空域和時(shí)域的聯(lián)合處理,可顯著提高機(jī)載雷達(dá)的雜波抑制能力。實(shí)際的雜波環(huán)境往往呈現(xiàn)出非均勻、非平穩(wěn)特性,傳統(tǒng)的采用與待檢測(cè)單元相鄰的樣本對(duì)協(xié)方差矩陣進(jìn)行最大似然估計(jì)的STAP算法性能受限,如何準(zhǔn)確有效地估計(jì)待檢測(cè)單元的雜波協(xié)方差矩陣,成為進(jìn)一步提升機(jī)載雷達(dá)目標(biāo)探測(cè)性能的關(guān)鍵問(wèn)題。本文研究非均勻雜波環(huán)境下的空時(shí)自適應(yīng)處理問(wèn)題,分析了非均勻環(huán)境下進(jìn)行數(shù)據(jù)樣本篩選的必要性,對(duì)現(xiàn)有樣本篩選算法進(jìn)行了仿真,并利用實(shí)測(cè)數(shù)據(jù)驗(yàn)證了處理算法的實(shí)際性能;在此基礎(chǔ)上,提出了一種改進(jìn)樣本自適應(yīng)樣本篩選算法,在小系統(tǒng)自由度情況下,具有更優(yōu)的篩選性能。本文的具體內(nèi)容概括如下:1.介紹機(jī)載雷達(dá)雜波回波模型及STAP基本理論。從全空時(shí)自由度處理的STAP算法出發(fā),討論了降維/降秩處理的必要性,介紹了常用降維STAP算法的基本原理,并對(duì)STAP處理性能的評(píng)估參數(shù)進(jìn)行說(shuō)明。2.對(duì)機(jī)載雷達(dá)的非均勻雜波環(huán)境,進(jìn)行信號(hào)建模與雜波仿真。對(duì)比分析了雜波的最小方差功率譜與傅里葉譜。介紹了非均勻環(huán)境下的雜波協(xié)方差矩陣估計(jì)方法及知識(shí)輔助STAP(KA-STAP)濾波器的基本原理。3.研究了兩種經(jīng)典的獨(dú)立同分布(IID)樣本篩選算法,即廣義內(nèi)積(GIP)法和基于傅里葉譜相似度(FSPS)的樣本篩選算法,討論了不同的非均勻雜波場(chǎng)景下,兩種算法的適用性,并進(jìn)行了建模仿真和性能對(duì)比。結(jié)果表明,GIP算法對(duì)存在少量離散強(qiáng)雜波單元的非均勻環(huán)境有較好的篩選效果,但在存在大量非均勻樣本的環(huán)境下,算法性能較差;FSPS算法則在強(qiáng)非均勻性雜波環(huán)境下呈現(xiàn)出良好的穩(wěn)健性。利用兩種算法對(duì)機(jī)載雷達(dá)實(shí)測(cè)數(shù)據(jù)進(jìn)行了處理,驗(yàn)證了算法的有效性。4.針對(duì)在小系統(tǒng)自由度下,已有的FSPS算法在污染樣本剔除及相似樣本選擇環(huán)節(jié)都存在分辨率不足的問(wèn)題,提出一種基于稀疏恢復(fù)技術(shù)的自適應(yīng)樣本篩選算法。該方法利用高精度稀疏恢復(fù)信息對(duì)參考單元樣本進(jìn)行篩選。相比FSPS算法,該方法在小系統(tǒng)自由度情況下可同時(shí)提升污染樣本剔除及相似樣本選擇時(shí)的性能。通過(guò)計(jì)算機(jī)仿真驗(yàn)證了該方法的有效性。
[Abstract]:Clutter suppression is the main problem in airborne radar target detection. The clutter suppression ability of airborne radar can be improved significantly. The traditional STAP algorithm using samples adjacent to the unit to estimate the covariance matrix has limited performance. How to estimate the clutter covariance matrix accurately and effectively is proposed. In this paper, the problem of space-time adaptive processing in heterogeneous clutter environment is studied, and the necessity of data sample selection in non-uniform environment is analyzed. The existing sample selection algorithm is simulated, and the actual performance of the algorithm is verified by using the measured data. On the basis of this, an improved sample adaptive sample screening algorithm is proposed, which can be used in the case of small system degrees of freedom. The detail contents of this paper are summarized as follows: 1. The clutter echo model of airborne radar and the basic theory of STAP are introduced. The necessity of dimension reduction / rank reduction processing is discussed based on the STAP algorithm of total space-time degree of freedom processing. This paper introduces the basic principle of commonly used dimensionally reduced STAP algorithm, and explains the evaluation parameters of STAP processing performance. 2. For the non-uniform clutter environment of airborne radar, The minimum variance power spectrum and Fourier spectrum of clutter are compared and analyzed. The estimation method of clutter covariance matrix and the basic principle of KAP KA-STAP filter in non-uniform environment are introduced. In this paper, two classical independent and distributed IID-based sample selection algorithms are studied. That is, the generalized inner product (GIP) method and the sample selection algorithm based on Fourier Spectrum similarity (FSPS). The applicability of the two algorithms in different heterogeneous clutter scenarios is discussed. The simulation and simulation results show that the GIP algorithm has a good screening effect on the non-uniform environment with a small number of discrete strong clutter elements, but in the presence of a large number of non-uniform samples, the GIP algorithm has a good selection effect on the non-uniform environment with a small number of discrete and strong clutter elements. The performance of the algorithm is poor and the FSPS algorithm shows good robustness in the environment of strong inhomogeneity clutter. Two algorithms are used to deal with the measured data of airborne radar, and the validity of the algorithm is verified. 4. In view of the small system degree of freedom, The existing FSPS algorithm has the problem of insufficient resolution in the selection of contaminated samples and similar samples. An adaptive sample selection algorithm based on sparse recovery technique is proposed. The method uses high precision sparse restoration information to filter reference unit samples. Compared with FSPS algorithm, the proposed algorithm is more efficient than FSPS algorithm. The proposed method can improve the performance of the proposed method for the removal of contaminated samples and the selection of similar samples at the same time in the case of small system degrees of freedom. The effectiveness of the proposed method is verified by computer simulation.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V243.2;TN957.51

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