陣列測向的稀疏超分辨方法研究
發(fā)布時間：2018-09-12 05:51
【摘要】：在現(xiàn)代復(fù)雜的空間電磁環(huán)境中,輻射源信號高度密集、目標(biāo)高速運動、“短,跳,隱”信號大量出現(xiàn),作為電子偵察與電子對抗重要組成部分的無線電測向定位遇到了前所未有的困難與挑戰(zhàn)。本文研究完善并利用壓縮感知理論,針對低信噪比、少快拍數(shù)、目標(biāo)運動等問題,構(gòu)建測向模型,設(shè)計稀疏重構(gòu)算法,提出了稀疏超分辨的陣列測向與方位角跟蹤的新方法。首先,基于?-約束等距性質(zhì)研究了?_1-analysis稀疏重構(gòu)的可重構(gòu)條件。針對目前?_1-analysis稀疏重構(gòu)所要滿足的?-約束等距性質(zhì)本質(zhì)上要求測量矩陣具有相關(guān)性很小這一限制,本文給出了?-約束等距約束常數(shù)的緊致上界,松弛了測量矩陣的相關(guān)性條件,為?_1-analysis稀疏重構(gòu)應(yīng)用于實際問題提供了理論保障。其次,提出了低信噪比環(huán)境下陣列接收信號的降噪恢復(fù)方法。在信噪比很低的情況下,傳統(tǒng)的空間譜估計方法和基于壓縮感知理論的稀疏測向方法的抗噪性能均不理想。本文建立了適用于陣列接收信號恢復(fù)的?_1-anaylsis重構(gòu)模型,證明了陣列接收模型中的流形矩陣滿足?_1-anaylsis稀疏重構(gòu)條件,從而在理論上保證了將?_1-analysis稀疏重構(gòu)用于低信噪比環(huán)境下恢復(fù)陣列接收信號的合理性,最后推導(dǎo)出了信號恢復(fù)誤差的理論上界。實驗表明該方法對提高低信噪比環(huán)境下的測向性能具有顯著效果。第三,分別提出了基于信號子空間與信號結(jié)構(gòu)信息的空間離散化陣列稀疏測向方法。目前大多數(shù)稀疏測向方法將陣列接收數(shù)據(jù)或其協(xié)方差矩陣作為稀疏重構(gòu)模型中的觀測數(shù)據(jù),測向的準(zhǔn)確性不理想,針對這一問題本文提出了利用信號子空間的陣列稀疏測向新方法,構(gòu)造了信號子空間的稀疏表示,建立了恢復(fù)輻射源信號能量的稀疏重構(gòu)模型,并將其轉(zhuǎn)換為二階錐規(guī)劃問題來求解。為了利用輻射源信號包括空間稀疏性和時間相關(guān)性在內(nèi)的信號結(jié)構(gòu)信息,本文提出了適用于陣列測向的塊稀疏貝葉斯方法,該方法同時結(jié)合了空間網(wǎng)格細(xì)化策略,在一定程度上緩解了大部分陣列稀疏測向方法所面臨的由空間離散化導(dǎo)致的輻射源目標(biāo)網(wǎng)格失配和稀疏表示模型誤差這一問題,進(jìn)一步提高了測向的性能。再次,提出了連續(xù)域陣列測向的稀疏超分辨方法。基于空間離散化的稀疏測向方法的性能受到網(wǎng)格失配效應(yīng)和稀疏表示模型誤差的限制,為了解決這一問題,本文基于無網(wǎng)格壓縮感知理論,利用單快拍數(shù)據(jù)實現(xiàn)了連續(xù)域稀疏超分辨測向,并給出了該方法實現(xiàn)超分辨測向所要滿足的輻射源最小角度距離和最少陣元個數(shù)。針對上述方法的測向性能受到相鄰輻射源之間最小角度距離和陣元數(shù)限制且僅能使用單個快拍數(shù)據(jù)使得性能受限等問題,本文提出了可以使用任意快拍數(shù)據(jù)實現(xiàn)連續(xù)空間上陣列測向的稀疏超分辨方法。最后,提出了基于壓縮穩(wěn)健主成分分析的運動目標(biāo)波達(dá)角跟蹤方法。目前鮮見專門針對運動目標(biāo)的波達(dá)角跟蹤的研究,本文利用壓縮穩(wěn)健主成分分析理論將波達(dá)角跟蹤問題可以轉(zhuǎn)化為一個低秩矩陣和稀疏矩陣恢復(fù)的問題,并設(shè)計了數(shù)值求解該優(yōu)化問題的線性交替方向法,實現(xiàn)了從陣列接收數(shù)據(jù)中恢復(fù)出固定目標(biāo)信號矩陣和運動目標(biāo)信號矩陣,并根據(jù)恢復(fù)的信號矩陣進(jìn)一步確定固定目標(biāo)的波達(dá)角估計、跟蹤運動目標(biāo)的波達(dá)方向。
[Abstract]:In the modern complex space electromagnetic environment, the radiator signal is highly dense, the target is moving at high speed, "short, hop, hidden" signals appear in large numbers, as an important part of electronic reconnaissance and electronic countermeasures, radio direction finding and positioning has encountered unprecedented difficulties and challenges. In this paper, a direction finding model is constructed and a sparse reconstruction algorithm is designed. A new method of sparse super-resolution array direction finding and azimuth tracking is proposed. Firstly, the reconfigurable condition of?_1-analysis sparse reconstruction is studied based on?-constraint equidistant property. The isometric property essentially requires that the measurement matrix has very little correlation. In this paper, a compact upper bound of the isometric constraint constant is given, which relaxes the correlation condition of the measurement matrix, and provides a theoretical guarantee for the application of the?_1-analysis sparse reconstruction to practical problems. Secondly, the array received signals in low SNR environment are proposed. In the case of low signal-to-noise ratio, the traditional spatial spectrum estimation method and sparse direction finding method based on compressed sensing theory have poor anti-noise performance. In this paper, a?_1-anaylsis reconstruction model for array received signal recovery is established, which proves that the manifold matrix in the array received model satisfies?_1-anaylsis sparse. The sparse reconstruction condition guarantees the rationality of applying?_1-analysis sparse reconstruction to recover the received signal of array in low SNR environment theoretically. Finally, the theoretical upper bound of the signal recovery error is deduced. Experiments show that this method has significant effect on improving the direction finding performance in low SNR environment. The sparse array direction finding method based on signal subspace and signal structure information is a sparse array direction finding method. Most of the sparse direction finding methods take the received array data or its covariance matrix as the observation data in the sparse reconstruction model. The accuracy of direction finding is not ideal. To solve this problem, this paper proposes the sparse array direction finding method based on signal subspace. The sparse representation of the signal subspace is constructed, and the sparse reconstruction model for recovering the energy of the emitter signal is established and converted into a second-order cone programming problem. Sparse Bayesian method, which combines spatial mesh refinement strategy, alleviates to a certain extent the problem of grid mismatch and sparse representation model error caused by spatial discretization in most sparse direction finding methods, and further improves the performance of direction finding. Thirdly, a continuous-domain array is proposed. Sparse super-resolution method for direction finding. Sparse direction finding method based on spatial discretization is limited by grid mismatch effect and sparse representation model error. To solve this problem, this paper uses single snapshot data to realize sparse super-resolution direction finding in continuous domain based on Meshless compressed sensing theory, and gives the method to realize super-resolution direction finding. In order to solve the problem that the minimum angle distance and the minimum number of array elements are required to satisfy the resolution of direction finding, which is limited by the minimum angle distance and the number of array elements between adjacent sources and can only use a single snapshot data, this paper proposes that the performance of the method is limited by using arbitrary snapshot data to achieve continuous space. Sparse super-resolution method for array direction finding. Finally, a method of DOA tracking for moving objects based on compressed robust principal component analysis is proposed. At present, there is little research on DOA tracking for moving objects. In this paper, the problem of DOA tracking can be transformed into a low rank matrix and sparse by using compressed robust principal component analysis theory. The linear alternating direction method is designed to solve the problem of matrix recovery. The fixed target signal matrix and the moving target signal matrix are recovered from the array receiving data, and the DOA estimation of the fixed target is further determined according to the recovered signal matrix to track the direction of arrival of the moving target.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN911.7
本文編號：2238095
[Abstract]:In the modern complex space electromagnetic environment, the radiator signal is highly dense, the target is moving at high speed, "short, hop, hidden" signals appear in large numbers, as an important part of electronic reconnaissance and electronic countermeasures, radio direction finding and positioning has encountered unprecedented difficulties and challenges. In this paper, a direction finding model is constructed and a sparse reconstruction algorithm is designed. A new method of sparse super-resolution array direction finding and azimuth tracking is proposed. Firstly, the reconfigurable condition of?_1-analysis sparse reconstruction is studied based on?-constraint equidistant property. The isometric property essentially requires that the measurement matrix has very little correlation. In this paper, a compact upper bound of the isometric constraint constant is given, which relaxes the correlation condition of the measurement matrix, and provides a theoretical guarantee for the application of the?_1-analysis sparse reconstruction to practical problems. Secondly, the array received signals in low SNR environment are proposed. In the case of low signal-to-noise ratio, the traditional spatial spectrum estimation method and sparse direction finding method based on compressed sensing theory have poor anti-noise performance. In this paper, a?_1-anaylsis reconstruction model for array received signal recovery is established, which proves that the manifold matrix in the array received model satisfies?_1-anaylsis sparse. The sparse reconstruction condition guarantees the rationality of applying?_1-analysis sparse reconstruction to recover the received signal of array in low SNR environment theoretically. Finally, the theoretical upper bound of the signal recovery error is deduced. Experiments show that this method has significant effect on improving the direction finding performance in low SNR environment. The sparse array direction finding method based on signal subspace and signal structure information is a sparse array direction finding method. Most of the sparse direction finding methods take the received array data or its covariance matrix as the observation data in the sparse reconstruction model. The accuracy of direction finding is not ideal. To solve this problem, this paper proposes the sparse array direction finding method based on signal subspace. The sparse representation of the signal subspace is constructed, and the sparse reconstruction model for recovering the energy of the emitter signal is established and converted into a second-order cone programming problem. Sparse Bayesian method, which combines spatial mesh refinement strategy, alleviates to a certain extent the problem of grid mismatch and sparse representation model error caused by spatial discretization in most sparse direction finding methods, and further improves the performance of direction finding. Thirdly, a continuous-domain array is proposed. Sparse super-resolution method for direction finding. Sparse direction finding method based on spatial discretization is limited by grid mismatch effect and sparse representation model error. To solve this problem, this paper uses single snapshot data to realize sparse super-resolution direction finding in continuous domain based on Meshless compressed sensing theory, and gives the method to realize super-resolution direction finding. In order to solve the problem that the minimum angle distance and the minimum number of array elements are required to satisfy the resolution of direction finding, which is limited by the minimum angle distance and the number of array elements between adjacent sources and can only use a single snapshot data, this paper proposes that the performance of the method is limited by using arbitrary snapshot data to achieve continuous space. Sparse super-resolution method for array direction finding. Finally, a method of DOA tracking for moving objects based on compressed robust principal component analysis is proposed. At present, there is little research on DOA tracking for moving objects. In this paper, the problem of DOA tracking can be transformed into a low rank matrix and sparse by using compressed robust principal component analysis theory. The linear alternating direction method is designed to solve the problem of matrix recovery. The fixed target signal matrix and the moving target signal matrix are recovered from the array receiving data, and the DOA estimation of the fixed target is further determined according to the recovered signal matrix to track the direction of arrival of the moving target.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN911.7
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