【摘要】：多輸入多輸出(Multiple-Input Multiple-Output,MIMO)雷達(dá)作為一種新體制雷達(dá),能夠運用空間分集和波形分集技術(shù)獲得更大的系統(tǒng)自由度,從而有效提高目標(biāo)探測能力和參數(shù)估計性能。MIMO雷達(dá)探測場景中目標(biāo)分布的稀疏性為壓縮感知(Compressed Sensing,CS)理論的應(yīng)用提供了條件,基于壓縮感知的MIMO雷達(dá)參數(shù)估計已成為雷達(dá)信號處理領(lǐng)域的研究熱點之一。本文將壓縮感知理論引入MIMO雷達(dá)系統(tǒng)架構(gòu),研究波形設(shè)計、陣列配置與壓縮感知測量的關(guān)系,提出基于發(fā)射波形、陣列構(gòu)型等效構(gòu)造測量矩陣的思路,并通過波形設(shè)計、陣列配置實現(xiàn)對測量矩陣的優(yōu)化,提升CS-MIMO雷達(dá)參數(shù)估計性能。本文的主要工作如下:研究了CS-MIMO雷達(dá)信號模型,闡述了壓縮感知基礎(chǔ)理論和CS-MIMO雷達(dá)參數(shù)估計原理;并對重構(gòu)性能影響因素進行分析,討論測量矩陣的構(gòu)造及優(yōu)化方法,提出基于發(fā)射波形、陣列構(gòu)型實現(xiàn)測量矩陣等效構(gòu)造的思路。利用混沌序列設(shè)計MIMO雷達(dá)發(fā)射信號,將混沌偽隨機發(fā)射信號作為測量算子,CS-MIMO雷達(dá)中測量矩陣的優(yōu)化設(shè)計問題便等效為波形優(yōu)化問題,有效降低系統(tǒng)模型的復(fù)雜度。為優(yōu)化頻譜形狀、抑制干擾噪聲,同時保證期望波形的隨機性、正交性,提出一種認(rèn)知MIMO雷達(dá)波形優(yōu)化設(shè)計算法。仿真實驗表明等效模型以及優(yōu)化算法的有效性,可明顯提高目標(biāo)參數(shù)估計的準(zhǔn)確度和精度。研究基于稀疏隨機線陣的CS-MIMO雷達(dá)信號模型,利用陣元位置的隨機性實現(xiàn)壓縮觀測,并證明所構(gòu)造的感知矩陣滿足非均勻重構(gòu)條件。為降低等效感知矩陣的列間相關(guān)性,提升算法的稀疏度上限和重構(gòu)性能,基于模擬退火算法對陣列進行優(yōu)化,實現(xiàn)測量矩陣的優(yōu)化。仿真實驗表明優(yōu)化陣列可提高目標(biāo)DOA重構(gòu)概率和估計精度。將一維稀疏隨機線陣擴展至二維L陣形式,研究基于稀疏隨機L陣的CS-MIMO雷達(dá)信號模型,證明陣列導(dǎo)引矢量矩陣可等效為測量矩陣并滿足非均勻重構(gòu)條件。研究基于粒子群算法的L陣優(yōu)化方法,對等效測量矩陣進行優(yōu)化,進一步降低感知矩陣列間相關(guān)性。仿真實驗表明該方法可有效提高CS-MIMO雷達(dá)二維角度估計性能。
[Abstract]:As a new system radar, multi-input multiple-output (Multiple-Input Multiple-Output,MIMO) radar can obtain greater degree of freedom by using spatial diversity and waveform diversity techniques. The sparsity of target distribution in MIMO radar detection scene provides the condition for the application of compressed perception (Compressed Sensing,CS) theory. MIMO radar parameter estimation based on compressed sensing has become one of the hot topics in the field of radar signal processing. In this paper, the compression sensing theory is introduced into the MIMO radar system architecture, and the waveform design, the relationship between array configuration and compression sensing measurement is studied, and the idea of constructing measurement matrix based on transmitting waveform and array configuration is put forward, and the waveform design is adopted. Array configuration can optimize the measurement matrix and improve the performance of CS-MIMO radar parameter estimation. The main work of this paper is as follows: the CS-MIMO radar signal model is studied, the basic theory of compression sensing and the principle of CS-MIMO radar parameter estimation are expounded, and the factors affecting the reconstruction performance are analyzed, and the construction and optimization method of the measurement matrix are discussed. In this paper, an equivalent structure of measurement matrix is proposed based on transmitting waveform and array configuration. Using chaotic sequence to design MIMO radar transmit signal, using chaotic pseudorandom transmit signal as measurement operator, the optimal design problem of measurement matrix in CS-MIMO radar is equivalent to waveform optimization problem, which effectively reduces the complexity of system model. In order to optimize the spectrum shape, suppress the interference noise and ensure the randomness and orthogonality of the desired waveform, a cognitive MIMO radar waveform optimization algorithm is proposed. Simulation results show that the effectiveness of the equivalent model and the optimization algorithm can significantly improve the accuracy and accuracy of the target parameter estimation. In this paper, the CS-MIMO radar signal model based on sparse random linear array is studied. The randomness of the position of the array elements is used to realize the compression observation, and it is proved that the constructed perceptual matrix satisfies the non-uniform reconstruction condition. In order to reduce the correlation between columns of the equivalent perceptual matrix and improve the sparse upper limit and reconstruction performance of the algorithm, the array is optimized based on simulated annealing algorithm, and the measurement matrix is optimized. Simulation results show that the optimized array can improve the probability and accuracy of target DOA reconstruction. The one-dimensional sparse random linear array is extended to 2-D L-matrix, and the CS-MIMO radar signal model based on sparse random L-matrix is studied. It is proved that the array guidance vector matrix can be equivalent to the measurement matrix and satisfy the condition of non-uniform reconstruction. The L matrix optimization method based on particle swarm optimization algorithm is studied to optimize the equivalent measurement matrix and further reduce the correlation between the columns of the perceptual matrix. Simulation results show that this method can effectively improve the performance of CS-MIMO radar two-dimensional angle estimation.
【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】：2017
【分類號】：TN958

【參考文獻】

相關(guān)期刊論文 前6條

1 彭珍妮;賁德;張弓;;基于混沌隨機濾波器的CS-MIMO雷達(dá)測量矩陣優(yōu)化設(shè)計[J];系統(tǒng)工程與電子技術(shù);2015年03期

2 李哲濤;潘田;朱更明;裴廷睿;;低冪平均列相關(guān)性測量矩陣構(gòu)造算法[J];電子學(xué)報;2014年07期

3 朱瑩;張弓;張勁東;;基于DCS的統(tǒng)計MIMO雷達(dá)信號模型及參數(shù)估計[J];雷達(dá)學(xué)報;2012年02期

4 趙瑞珍;秦周;胡紹海;;一種基于特征值分解的測量矩陣優(yōu)化方法[J];信號處理;2012年05期

5 潘匯;張勁東;張弓;;壓縮感知雷達(dá)感知矩陣優(yōu)化[J];數(shù)據(jù)采集與處理;2012年02期

6 焦李成;楊淑媛;劉芳;侯彪;;壓縮感知回顧與展望[J];電子學(xué)報;2011年07期

相關(guān)博士學(xué)位論文 前1條

1 杜小勇;稀疏成份分析及在雷達(dá)成像處理中的應(yīng)用[D];國防科學(xué)技術(shù)大學(xué);2005年

相關(guān)碩士學(xué)位論文 前1條

1 任艷春;混沌理論在雷達(dá)通信中的應(yīng)用研究[D];哈爾濱工程大學(xué);2007年

，

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