發(fā)布時(shí)間：2018-05-08 09:07

  本文選題：MIMO雷達(dá) + 目標(biāo)檢測(cè)��； 參考：《吉林大學(xué)》2014年碩士論文

【摘要】：多輸入多輸出（MIMO）雷達(dá)在目標(biāo)檢測(cè)、參數(shù)估計(jì)和目標(biāo)識(shí)別等領(lǐng)域具有諸多優(yōu)勢(shì)。由于采用了分集的發(fā)射信號(hào)，MIMO雷達(dá)可顯著提高目標(biāo)的檢測(cè)性能。 本文首先針對(duì)基于極化分集技術(shù)的MIMO雷達(dá)目標(biāo)檢測(cè)問題展開了深入研究。傳統(tǒng)MIMO雷達(dá)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(dá)在小目標(biāo)和雜波背景下具有更強(qiáng)的檢測(cè)能力。不同的極化方式會(huì)帶來檢測(cè)性能的較大差異，通過發(fā)射端極化波形的優(yōu)化設(shè)計(jì)，可明顯改善MIMO雷達(dá)的檢測(cè)性能。進(jìn)而，本文研究了基于隨機(jī)矩陣?yán)碚摚≧MT）的MIMO雷達(dá)目標(biāo)檢測(cè)問題。目前MIMO雷達(dá)目標(biāo)檢測(cè)大多是在采樣數(shù)遠(yuǎn)大于陣元數(shù)的假設(shè)前提下進(jìn)行的，當(dāng)樣本數(shù)不充足時(shí)導(dǎo)致性能降低。隨機(jī)矩陣?yán)碚摓镸IMO雷達(dá)信號(hào)處理提供了一個(gè)便利的工具，在噪聲方差和目標(biāo)散射矩陣未知的環(huán)境下，基于隨機(jī)矩陣漸進(jìn)譜理論（AST）的方法可實(shí)現(xiàn)雙基地MIMO雷達(dá)目標(biāo)的盲檢測(cè)。本論文的研究工作得到國(guó)家自然基金項(xiàng)目“基于極化分集的MIMO雷達(dá)參數(shù)聯(lián)合估計(jì)與目標(biāo)定位”（項(xiàng)目編號(hào)：61071140）和“基于大維隨機(jī)矩陣的MIMO雷達(dá)穩(wěn)健目標(biāo)檢測(cè)與估計(jì)”（項(xiàng)目編號(hào)：61371158）的資助。本文主要研究工作如下： 在傳統(tǒng)分布式MIMO雷達(dá)模型的基礎(chǔ)上，對(duì)雜波背景下基于極化分集的MIMO雷達(dá)目標(biāo)檢測(cè)問題進(jìn)行了研究。建立了極化MIMO雷達(dá)目標(biāo)檢測(cè)的信號(hào)模型，提出了一種基于Jones矢量的極化MIMO雷達(dá)檢測(cè)算法。該方法利用發(fā)射天線波形極化的多重搜索，實(shí)現(xiàn)了極化波形的優(yōu)化。仿真結(jié)果驗(yàn)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(biāo)檢測(cè)的性能。 為降低多重搜索的復(fù)雜度，本文進(jìn)一步提出一種基于螢火蟲群優(yōu)化（GSO）的MIMO雷達(dá)目標(biāo)檢測(cè)算法。該方法以檢測(cè)概率最大為目標(biāo)函數(shù)進(jìn)行發(fā)射極化波形的選擇，利用GSO算法進(jìn)行多維并行搜索，通過并行處理數(shù)據(jù)，同時(shí)優(yōu)化多個(gè)極化參數(shù)，解決了難以實(shí)現(xiàn)的多重嵌套搜索問題。對(duì)算法的仿真結(jié)果表明，基于GSO的MIMO雷達(dá)目標(biāo)檢測(cè)算法改善了檢測(cè)性能，減少了計(jì)算量。 上述MIMO雷達(dá)檢測(cè)方法盡管提高了目標(biāo)檢測(cè)性能，然而，當(dāng)采樣數(shù)不足或采樣數(shù)與陣元數(shù)接近時(shí)其性能將會(huì)下降。鑒于此，本文從雙基地MIMO雷達(dá)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(jī)矩陣?yán)碚摰腗IMO雷達(dá)目標(biāo)檢測(cè)算法。該方法在目標(biāo)散射信息與定位信息及噪聲方差未知的情況下，利用隨機(jī)矩陣?yán)碚搶?shí)現(xiàn)了目標(biāo)的盲檢測(cè)。該算法對(duì)先驗(yàn)要求大大放松，對(duì)噪聲變化不敏感，實(shí)現(xiàn)了大陣列情況下MIMO雷達(dá)的穩(wěn)健目標(biāo)檢測(cè)。
[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(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙建中;陳歆煒;;利用相對(duì)極化散射矩陣對(duì)目標(biāo)進(jìn)行分類的研究[J];重慶郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年06期

2 晁淑媛;陳伯孝;戴奉周;;非均勻雜波MIMO雷達(dá)檢測(cè)[J];電子學(xué)報(bào);2011年03期

3 曹學(xué)軍,李永禎,姜文利,莊釗文;一種改進(jìn)的高分辨極化目標(biāo)檢測(cè)算法[J];電子與信息學(xué)報(bào);2002年07期

4 張西川;張永順;謝文沖;王永良;;機(jī)載相干MIMO雷達(dá)雜波自由度估計(jì)研究[J];電子與信息學(xué)報(bào);2011年09期

5 常帥;李宏;徐長(zhǎng)輝;;MIMO雷達(dá)檢測(cè)性能分析[J];電子設(shè)計(jì)工程;2011年24期

6 王克讓;朱曉華;何勁;;基于矢量傳感器MIMO雷達(dá)的DOD DOA和極化聯(lián)合估計(jì)算法[J];電子與信息學(xué)報(bào);2012年01期

7 鄭桂妹;陳伯孝;楊明磊;;基于矢量傳感器MIMO雷達(dá)的發(fā)射極化優(yōu)化DOA估計(jì)算法[J];電子與信息學(xué)報(bào);2014年03期

8 馮訊;王首勇;朱曉波;;基于粒子濾波的MIMO雷達(dá)目標(biāo)似然比檢測(cè)方法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年02期

9 汪霜玲;何茜;何子述;;非高斯雜波下分置天線MIMO雷達(dá)的動(dòng)目標(biāo)檢測(cè)[J];計(jì)算機(jī)應(yīng)用研究;2011年02期

10 董云龍;黃勇;關(guān)鍵;;非均勻雜波中統(tǒng)計(jì)MIMO雷達(dá)的相參檢測(cè)[J];中國(guó)電子科學(xué)研究院學(xué)報(bào);2011年02期

相關(guān)博士學(xué)位論文 前3條

1 張浩川;隨機(jī)矩陣?yán)碚撆c多入多出通信系統(tǒng)的性能分析[D];北京郵電大學(xué);2011年

2 周生華;分集MIMO雷達(dá)目標(biāo)散射特性與檢測(cè)算法[D];西安電子科技大學(xué);2011年

3 景源;MIMO-OFDM無線通信系統(tǒng)中信道估計(jì)和頻率同步的研究[D];大連理工大學(xué);2010年

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