Krylov子空間多通道參數(shù)化自適應(yīng)信號檢測方法
發(fā)布時(shí)間：2018-10-26 10:15
【摘要】：多通道信號檢測問題是雷達(dá)、通信和醫(yī)療等領(lǐng)域的主要研究課題之一,經(jīng)典的信號檢測方法存在的問題主要有全空時(shí)處理計(jì)算量大,實(shí)際非均勻環(huán)境中符合獨(dú)立同分布條件的訓(xùn)練樣本較少,由此導(dǎo)致信號檢測性能下降以及實(shí)時(shí)運(yùn)算困難。本文針對經(jīng)典多通道信號檢測方法中存在的問題,采用Krylov子空間方法迭代地計(jì)算空時(shí)二維權(quán)向量,并利用雜波數(shù)據(jù)在脈沖域的平穩(wěn)性,將雜波近似為AR模型。在此基礎(chǔ)上,研究Krylov子空間自適應(yīng)匹配濾波器和Krylov子空間多通道參數(shù)化自適應(yīng)信號檢測方法,主要工作及相關(guān)結(jié)論包括:1、將Krylov子空間方法應(yīng)用于AMF檢測器,并分析迭代過程中產(chǎn)生的一系列KAMF檢測器的虛警概率。雜波協(xié)方差矩陣具有低秩校正結(jié)構(gòu)形式時(shí),采用共軛梯度法收斂速度快,且迭代至r(10)1次時(shí)有較好的檢測性能。根據(jù)極端Ritz值的快速收斂性、?R-正交投影定理和Wishart分布分析KAMF檢測器在各迭代次數(shù)下的虛警概率,并給出1?k?r(10)1時(shí)的近似理論表達(dá)式�；诜抡婕皩�(shí)測數(shù)據(jù)對虛警概率及檢測概率作驗(yàn)證,理論分析及仿真結(jié)果均說明KAMF檢測器的虛警概率介于MF和AMF之間。同時(shí),迭代次數(shù)k(28)r(10)1時(shí),檢測概率優(yōu)于AMF。2、將Krylov子空間方法應(yīng)用于多通道參數(shù)化自適應(yīng)信號檢測,采用共軛梯度法解Wiener-Hopf方程,得到一系列的KPAMF檢測器。迭代完成時(shí),KPAMF與PAMF的檢測性能一致,且多數(shù)情況下,KPAMF能在較少的迭代次數(shù)內(nèi)收斂,達(dá)到進(jìn)一步降低運(yùn)算量的目的。同時(shí),雜波協(xié)方差矩陣的條件數(shù)較大時(shí),采用預(yù)處理的共軛梯度法可降低條件數(shù),提高檢測器收斂速度。另一方面,對干擾占主導(dǎo)地位的雜波,AR模型的階數(shù)、參數(shù)與干擾個(gè)數(shù)及干擾參數(shù)有關(guān),且預(yù)測向量的自相關(guān)矩陣具備低秩校正結(jié)構(gòu)。基于AR仿真數(shù)據(jù)、干擾模型及實(shí)測數(shù)據(jù)論證了以上KPAMF檢測器的相關(guān)性質(zhì)及方法。3、對功率譜非均勻和統(tǒng)計(jì)分布非均勻的雜波環(huán)境建模,并將KAMF檢測器和KPAMF檢測器應(yīng)用于非均勻環(huán)境中的目標(biāo)檢測。仿真結(jié)果表明,由于非均勻環(huán)境下有效訓(xùn)練樣本不足,KPAMF檢測器和PAMF的雜波抑制效果優(yōu)于KAMF檢測器和AMF。同時(shí),KAMF檢測器在特定迭代次數(shù)下的檢測效果優(yōu)于AMF,KPAMF檢測器在較少的迭代次數(shù)內(nèi)接近于PAMF的檢測效果,論證了KAMF檢測器和KPAMF檢測器的相關(guān)性質(zhì)及結(jié)論。
[Abstract]:The problem of multi-channel signal detection is one of the main research topics in the fields of radar, communication and medical treatment. In the non-uniform environment, there are fewer training samples which meet the condition of independent and same distribution, which leads to the deterioration of signal detection performance and the difficulty of real-time operation. In order to solve the problems in classical multi-channel signal detection methods, Krylov subspace method is used to iteratively calculate space-time two rights vector, and the clutter is approximated to AR model by using the smoothness of clutter data in pulse domain. On this basis, the Krylov subspace adaptive matched filter and the Krylov subspace multi-channel parameterized adaptive signal detection method are studied. The main work and related conclusions are as follows: 1. The Krylov subspace method is applied to the AMF detector. The false alarm probability of a series of KAMF detectors generated during iteration is analyzed. When the clutter covariance matrix has a low rank correction structure, the conjugate gradient method has a fast convergence rate and good detection performance when iterated to r (10) once. According to the fast convergence of extreme Ritz values,? R- orthogonal projection theorem and Wishart distribution, the false alarm probability of KAMF detector under each iteration is analyzed, and the approximate theoretical expression of 1?k?r (10) 1 is given. The false alarm probability and detection probability are verified based on simulated and measured data. The theoretical analysis and simulation results show that the false alarm probability of KAMF detector is between MF and AMF. At the same time, when the number of iterations k (28) r (10) is 1, the detection probability is better than that of AMF.2,. The Krylov subspace method is applied to multi-channel parameterized adaptive signal detection. The conjugate gradient method is used to solve the Wiener-Hopf equation and a series of KPAMF detectors are obtained. When the iteration is completed, the detection performance of KPAMF and PAMF is the same, and in most cases, KPAMF can converge in a few iterations, so as to further reduce the computational complexity. At the same time, when the condition number of clutter covariance matrix is large, the condition number can be reduced and the convergence rate of detector can be improved by using the conjugate gradient method of preprocessing. On the other hand, for clutter dominated by interference, the order and parameter of AR model are related to the number of disturbances and interference parameters, and the autocorrelation matrix of prediction vector has low rank correction structure. Based on the AR simulation data, interference model and measured data, the related properties and methods of the above KPAMF detectors are demonstrated. 3. The clutter environment with non-uniform power spectrum and statistical distribution is modeled. KAMF detector and KPAMF detector are applied to target detection in non-uniform environment. The simulation results show that the clutter suppression effect of KPAMF detector and PAMF is better than that of KAMF detector and AMF. because of the shortage of effective training samples in non-uniform environment. At the same time, the detection effect of KAMF detector is better than that of AMF,KPAMF detector in less iterations than that of PAMF detector under certain iterations. The properties and conclusions of KAMF detector and KPAMF detector are proved.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.23
本文編號：2295389
[Abstract]:The problem of multi-channel signal detection is one of the main research topics in the fields of radar, communication and medical treatment. In the non-uniform environment, there are fewer training samples which meet the condition of independent and same distribution, which leads to the deterioration of signal detection performance and the difficulty of real-time operation. In order to solve the problems in classical multi-channel signal detection methods, Krylov subspace method is used to iteratively calculate space-time two rights vector, and the clutter is approximated to AR model by using the smoothness of clutter data in pulse domain. On this basis, the Krylov subspace adaptive matched filter and the Krylov subspace multi-channel parameterized adaptive signal detection method are studied. The main work and related conclusions are as follows: 1. The Krylov subspace method is applied to the AMF detector. The false alarm probability of a series of KAMF detectors generated during iteration is analyzed. When the clutter covariance matrix has a low rank correction structure, the conjugate gradient method has a fast convergence rate and good detection performance when iterated to r (10) once. According to the fast convergence of extreme Ritz values,? R- orthogonal projection theorem and Wishart distribution, the false alarm probability of KAMF detector under each iteration is analyzed, and the approximate theoretical expression of 1?k?r (10) 1 is given. The false alarm probability and detection probability are verified based on simulated and measured data. The theoretical analysis and simulation results show that the false alarm probability of KAMF detector is between MF and AMF. At the same time, when the number of iterations k (28) r (10) is 1, the detection probability is better than that of AMF.2,. The Krylov subspace method is applied to multi-channel parameterized adaptive signal detection. The conjugate gradient method is used to solve the Wiener-Hopf equation and a series of KPAMF detectors are obtained. When the iteration is completed, the detection performance of KPAMF and PAMF is the same, and in most cases, KPAMF can converge in a few iterations, so as to further reduce the computational complexity. At the same time, when the condition number of clutter covariance matrix is large, the condition number can be reduced and the convergence rate of detector can be improved by using the conjugate gradient method of preprocessing. On the other hand, for clutter dominated by interference, the order and parameter of AR model are related to the number of disturbances and interference parameters, and the autocorrelation matrix of prediction vector has low rank correction structure. Based on the AR simulation data, interference model and measured data, the related properties and methods of the above KPAMF detectors are demonstrated. 3. The clutter environment with non-uniform power spectrum and statistical distribution is modeled. KAMF detector and KPAMF detector are applied to target detection in non-uniform environment. The simulation results show that the clutter suppression effect of KPAMF detector and PAMF is better than that of KAMF detector and AMF. because of the shortage of effective training samples in non-uniform environment. At the same time, the detection effect of KAMF detector is better than that of AMF,KPAMF detector in less iterations than that of PAMF detector under certain iterations. The properties and conclusions of KAMF detector and KPAMF detector are proved.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.23
【參考文獻(xiàn)】
相關(guān)期刊論文 前5條
1 吳洪;王永良;陳建文;;線性預(yù)測類STAP方法研究[J];電子與信息學(xué)報(bào);2008年05期
2 任雙橋;劉永祥;黎湘;莊釗文;;廣義相關(guān)K分布雜波建模與仿真[J];自然科學(xué)進(jìn)展;2006年06期
3 丁前軍,王永良,張永順;降秩自適應(yīng)濾波算法研究[J];雷達(dá)科學(xué)與技術(shù);2005年04期
4 方學(xué)立,梁甸農(nóng);一種用于相關(guān)高斯序列仿真的新方法[J];雷達(dá)科學(xué)與技術(shù);2004年04期
5 湯俊,彭應(yīng)寧;共軛梯度自適應(yīng)波束形成算法[J];電子科學(xué)學(xué)刊;2000年01期
相關(guān)博士學(xué)位論文 前1條
1 唐斌;機(jī)載雷達(dá)Krylov子空間STAP算法研究[D];電子科技大學(xué);2008年
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