本文選題：MIMO雷達 + 目標檢測��； 參考：《吉林大學》2014年碩士論文

【摘要】：多輸入多輸出（MIMO）雷達在目標檢測、參數(shù)估計和目標識別等領(lǐng)域具有諸多優(yōu)勢。由于采用了分集的發(fā)射信號，MIMO雷達可顯著提高目標的檢測性能。 本文首先針對基于極化分集技術(shù)的MIMO雷達目標檢測問題展開了深入研究。傳統(tǒng)MIMO雷達主要采用空間分集技術(shù)，而極化分集技術(shù)的利用使雷達在小目標和雜波背景下具有更強的檢測能力。不同的極化方式會帶來檢測性能的較大差異，通過發(fā)射端極化波形的優(yōu)化設(shè)計，可明顯改善MIMO雷達的檢測性能。進而，本文研究了基于隨機矩陣理論（RMT）的MIMO雷達目標檢測問題。目前MIMO雷達目標檢測大多是在采樣數(shù)遠大于陣元數(shù)的假設(shè)前提下進行的，當樣本數(shù)不充足時導致性能降低。隨機矩陣理論為MIMO雷達信號處理提供了一個便利的工具，在噪聲方差和目標散射矩陣未知的環(huán)境下，基于隨機矩陣漸進譜理論（AST）的方法可實現(xiàn)雙基地MIMO雷達目標的盲檢測。本論文的研究工作得到國家自然基金項目“基于極化分集的MIMO雷達參數(shù)聯(lián)合估計與目標定位”（項目編號：61071140）和“基于大維隨機矩陣的MIMO雷達穩(wěn)健目標檢測與估計”（項目編號：61371158）的資助。本文主要研究工作如下： 在傳統(tǒng)分布式MIMO雷達模型的基礎(chǔ)上，對雜波背景下基于極化分集的MIMO雷達目標檢測問題進行了研究。建立了極化MIMO雷達目標檢測的信號模型，提出了一種基于Jones矢量的極化MIMO雷達檢測算法。該方法利用發(fā)射天線波形極化的多重搜索，實現(xiàn)了極化波形的優(yōu)化。仿真結(jié)果驗證了算法的有效性，與水平、垂直、正交極化方式相比，，該算法改善了目標檢測的性能。 為降低多重搜索的復雜度，本文進一步提出一種基于螢火蟲群優(yōu)化（GSO）的MIMO雷達目標檢測算法。該方法以檢測概率最大為目標函數(shù)進行發(fā)射極化波形的選擇，利用GSO算法進行多維并行搜索，通過并行處理數(shù)據(jù)，同時優(yōu)化多個極化參數(shù)，解決了難以實現(xiàn)的多重嵌套搜索問題。對算法的仿真結(jié)果表明，基于GSO的MIMO雷達目標檢測算法改善了檢測性能，減少了計算量。 上述MIMO雷達檢測方法盡管提高了目標檢測性能，然而，當采樣數(shù)不足或采樣數(shù)與陣元數(shù)接近時其性能將會下降。鑒于此，本文從雙基地MIMO雷達模型出發(fā)，在采樣數(shù)與收發(fā)陣元數(shù)的乘積接近的情況下，提出一種基于隨機矩陣理論的MIMO雷達目標檢測算法。該方法在目標散射信息與定位信息及噪聲方差未知的情況下，利用隨機矩陣理論實現(xiàn)了目標的盲檢測。該算法對先驗要求大大放松，對噪聲變化不敏感，實現(xiàn)了大陣列情況下MIMO雷達的穩(wěn)健目標檢測。
[Abstract]:Multi-input multiple-output MIMO-radar has many advantages in target detection, parameter estimation and target recognition. Because of the diversity of transmit signal MIMO radar can significantly improve the detection performance of the target. In this paper, the problem of MIMO radar target detection based on polarization diversity is studied. The traditional MIMO radar mainly uses space diversity technology, but the use of polarization diversity technology makes radar have stronger detection ability in the background of small target and clutter. Different polarization modes will lead to great differences in detection performance. The detection performance of MIMO radar can be improved obviously by optimizing the polarization waveform of the transmitter. Furthermore, the problem of MIMO radar target detection based on random matrix theory is studied in this paper. At present, MIMO radar target detection is mostly based on the assumption that the number of samples is much larger than the number of array elements, and the performance is degraded when the number of samples is not sufficient. Stochastic matrix theory provides a convenient tool for signal processing of MIMO radar. Under the condition of unknown noise variance and target scattering matrix, blind detection of bistatic MIMO radar targets can be realized based on stochastic matrix asymptotic spectrum theory. In this paper, the National Fund for Nature Project "Joint estimation and Target location of MIMO Radar parameters based on polarization Diversity" (Project No.: 61071140) and "robust Target Detection and estimation of MIMO Radar based on large Dimension Random Matrix" "(item No. 61371158). The main work of this paper is as follows: Based on the traditional distributed MIMO radar model, the problem of MIMO radar target detection based on polarization diversity in clutter background is studied. The signal model of polarimetric MIMO radar target detection is established, and a polarimetric MIMO radar detection algorithm based on Jones vector is proposed. In this method, the polarization waveform of transmitting antenna is optimized by multiple search. Simulation results verify the effectiveness of the algorithm. Compared with horizontal, vertical and orthogonal polarization, the algorithm improves the performance of target detection. In order to reduce the complexity of multiple search, a MIMO radar target detection algorithm based on firefly swarm optimization (GSO) is proposed in this paper. In this method, the maximum detection probability is taken as the objective function to select the transmitted polarization waveform, and the multi-dimensional parallel search is carried out by using GSO algorithm, and the data is processed in parallel, and several polarization parameters are optimized at the same time. It solves the problem of multi-nested search which is difficult to implement. The simulation results show that the MIMO radar target detection algorithm based on GSO improves the detection performance and reduces the computational complexity. Although the above MIMO radar detection method improves the performance of target detection, its performance will decline when the number of samples is insufficient or the number of samples is close to the number of array elements. In this paper, based on the bistatic MIMO radar model, a MIMO radar target detection algorithm based on random matrix theory is proposed under the condition that the product of sampling number and transceiver element number is close to each other. Under the condition that the scattering information and location information and the variance of noise are unknown, the blind detection of target is realized by using the stochastic matrix theory. The algorithm is not sensitive to noise changes and greatly relaxes the priori requirements, and realizes robust target detection in large array MIMO radar.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.51

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