本文選題：MIMO雷達　切入點：角度估計　出處：《南京航空航天大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：多輸入多輸出(Multiple Input Multiple Output,MIMO)雷達一種新體制雷達,其系統(tǒng)的優(yōu)越性,對深入理解傳統(tǒng)意義上的雷達,以及新概念雷達的研究具有指導(dǎo)意義,而壓縮感知(Compressed Sensing,CS)理論作為一種新興的信號處理技術(shù),已經(jīng)被眾多研究者應(yīng)用在雷達領(lǐng)域的信號處理中。本文將壓縮感知理論應(yīng)用于MIMO雷達目標(biāo)的角度估計中,主要工作如下:1、MIMO雷達空間譜估計的研究。將傳統(tǒng)空間譜估計方法,如Capon,MUSIC以及ESPRIT,推廣到雙基地的MIMO雷達目標(biāo)(Direction Of Department,DOD)和(Direction Of Arrival,DOA)的估計中。此外,針對MIMO雷達在色噪聲環(huán)境中的角度估計問題,引入高階累積量,提出了一種基于改進四階累積量的角度估計方法。在保證虛擬陣列孔徑有效擴展的前提下去除回波信號中的冗余項,達到矩陣降維的目的,最后利用MUSIC-like算法進行譜峰搜索得到估計值。所提算法能夠有效抑制高斯色噪聲,在保證估計精度的基礎(chǔ)上,能夠減少四階累積量矩陣的維數(shù),計算的復(fù)雜度也得到降低。2、將壓縮感知理論應(yīng)用于MIMO雷達目標(biāo)角度估計的問題中,提出一種基于稀疏重構(gòu)的MIMO雷達DOD和DOA聯(lián)合估計方法。首先在二維角度空間中構(gòu)造冗余字典;進行協(xié)方差矩陣的特征分解,從中選取有效的特征向量在該冗余字典下稀疏表示,構(gòu)建低維稀疏線性模型;最后通過重構(gòu)算法得到目標(biāo)的角度信息。該方法對特征向量的稀疏重構(gòu)降低了重構(gòu)原始接受信號的計算復(fù)雜度,且在低信噪比和低快拍下仍有較好的估計性能。3、將基于高階累積量的算法與壓縮感知理論相結(jié)合,提出一種基于高階累積量與稀疏表示的MIMO雷達收發(fā)角度估計方法。首先利用四階累積量對高斯噪聲不敏感的特性對信號進行降噪處理;然后對四階累積量矩陣進行特征分解得到信號子空間,通過將其中的特征向量在合適的冗余字典上稀疏表示,通過重構(gòu)算法求解稀疏系數(shù),進而聯(lián)合估計目標(biāo)的收發(fā)角度。仿真結(jié)果表明,所提方法不僅能夠有效抑制高斯色噪聲,而且具有較高的穩(wěn)健性。
[Abstract]:Multiple-Input-Multiple-output Input Multiple OutputMimo Radar A new system radar, the superiority of its system is of guiding significance for the deep understanding of the traditional radar and the research of the new concept radar. As a new signal processing technology, compressed sensing theory has been used by many researchers in the field of radar signal processing. In this paper, the compressed sensing theory is applied to the angle estimation of MIMO radar targets. The main work of this paper is as follows: 1) the research on spatial spectrum estimation of MIMO radar is as follows. The traditional methods of spatial spectrum estimation, such as CaponMUSIC and Esprit, are extended to the estimation of bistatic MIMO radar targets (DOD) and Direction of of ArrivalDOAs. To solve the problem of angle estimation of MIMO radar in colored noise environment, a high order cumulant is introduced. An angle estimation method based on the improved fourth order cumulant is proposed. The redundant items in the echo signal are removed under the premise of effective expansion of the virtual array aperture, so as to achieve the purpose of reducing the dimension of the matrix. Finally, the spectral peak search of MUSIC-like algorithm is used to obtain the estimated value. The proposed algorithm can effectively suppress Gao Si color noise and reduce the dimension of the fourth order cumulant matrix on the basis of guaranteeing the estimation accuracy. The computational complexity is also reduced. The compressed sensing theory is applied to the problem of MIMO radar target angle estimation. A sparse reconstruction based joint estimation method for MIMO radar DOD and DOA is proposed. Firstly, redundant dictionaries are constructed in two-dimensional angle space. The eigenvalues of the covariance matrix are decomposed, and the effective eigenvector is selected to be sparse representation in the redundant dictionary, and the low-dimensional sparse linear model is constructed. Finally, the angle information of the target is obtained by the reconstruction algorithm, which reduces the computational complexity of the original received signal reconstruction by sparse reconstruction of the eigenvector. And it still has good estimation performance under low SNR and low shot. It combines the algorithm based on high order cumulant with the theory of compression perception. In this paper, a method of MIMO radar transceiver angle estimation based on high order cumulant and sparse representation is proposed. Firstly, the fourth order cumulant is not sensitive to Gao Si noise. Then the fourth order cumulant matrix is decomposed to obtain the signal subspace. The eigenvector is represented sparsely in the appropriate redundant dictionary and the sparse coefficient is solved by the reconstruction algorithm. The simulation results show that the proposed method not only can effectively suppress Gao Si color noise, but also has high robustness.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.51
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本文編號：1614240