【摘要】：多輸入多輸出(MIMO)雷達(dá)作為一種新體制雷達(dá),與傳統(tǒng)雷達(dá)相比,由于維數(shù)的增加面臨著海量數(shù)據(jù)處理等問(wèn)題。而雷達(dá)探測(cè)目標(biāo)相對(duì)于背景的高度稀疏為壓縮感知技術(shù)的應(yīng)用提供了可能,基于壓縮感知技術(shù)的雷達(dá)信號(hào)處理已成為雷達(dá)領(lǐng)域研究熱點(diǎn)。在低信噪比探測(cè)條件下,壓縮感知的重構(gòu)性能將大幅度降低甚至失效,并且感知矩陣的等距同構(gòu)條件使得脈沖積累技術(shù)難以實(shí)現(xiàn)。本文針對(duì)如何在壓縮感知MIMO雷達(dá)中實(shí)現(xiàn)多脈沖信息的利用來(lái)提高信噪比,研究了基于壓縮感知的MIMO雷達(dá)空時(shí)回波信號(hào)聯(lián)合稀疏表示方式,以及一種針對(duì)壓縮感知雷達(dá)脈沖積累過(guò)程中基于聯(lián)合稀疏字典同步的目標(biāo)距離走動(dòng)補(bǔ)償方法,以提高低信噪比壓縮感知MIMO雷達(dá)的重構(gòu)性能。主要工作如下:(1)研究了在脈沖體制下的MIMO雷達(dá)空時(shí)回波信號(hào)高維聯(lián)合稀疏表示模型。通過(guò)分析空時(shí)回波信號(hào)三維數(shù)據(jù)塊的切片方式,研究了空時(shí)回波信號(hào)多脈沖切片高維聯(lián)合稀疏字典的三種構(gòu)造方法,設(shè)計(jì)了一種稀疏性最優(yōu)的聯(lián)合稀疏表示模型。為MIMO雷達(dá)空時(shí)回波高維聯(lián)合稀疏重構(gòu)提供了基礎(chǔ)。(2)針對(duì)單目標(biāo)脈沖組間跨距離單元走動(dòng)問(wèn)題,提出了基于聯(lián)合稀疏字典脈組對(duì)齊的目標(biāo)距離走動(dòng)補(bǔ)償方法。通過(guò)對(duì)聯(lián)合稀疏字典脈組對(duì)齊,矯正目標(biāo)跨距離單元走動(dòng),在組內(nèi)脈沖積累的基礎(chǔ)上,實(shí)現(xiàn)脈沖組間積累。仿真實(shí)驗(yàn)驗(yàn)證,所提方法提高了跨距離單元走動(dòng)下壓縮感知雷達(dá)運(yùn)動(dòng)目標(biāo)參數(shù)估計(jì)性能。(3)分別研究了空時(shí)高維聯(lián)合稀疏字典構(gòu)造,以及基于聯(lián)合稀疏字典脈組對(duì)齊的目標(biāo)距離走動(dòng)補(bǔ)償方法在集中式和分布式MIMO雷達(dá)的運(yùn)動(dòng)目標(biāo)參數(shù)估計(jì)中的應(yīng)用。設(shè)計(jì)了集中式和分布式MIMO雷達(dá)的空時(shí)高維聯(lián)合稀疏表示模型和相應(yīng)的聯(lián)合稀疏字典脈組對(duì)齊方法,實(shí)現(xiàn)了低信噪比下集中式和分布式壓縮感知MIMO雷達(dá)運(yùn)動(dòng)目標(biāo)多參數(shù)聯(lián)合估計(jì)。
[Abstract]:As a new type of radar, multi-input multi-output (MIMO) radar is confronted with the problems of massive data processing due to the increase of dimension compared with the traditional radar. The high sparsity of radar target relative to background provides the possibility for the application of compressed sensing technology. Radar signal processing based on compressed sensing technology has become a hot research area in radar field. Under the condition of low signal-to-noise ratio (SNR) detection, the reconstruction performance of compressed sensing will be greatly reduced or even invalidated, and the equidistant isomorphism of the sensing matrix makes it difficult to realize the pulse accumulation technique. In order to improve the signal-to-noise ratio (SNR) of multi-pulse information in compressed sensing MIMO radar, the combined sparse representation of space-time echo signal of MIMO radar based on compressed sensing is studied in this paper. In order to improve the reconstruction performance of compressed perceptual MIMO radar with low SNR, a compensation method for target range walk based on joint sparse dictionary synchronization in pulse accumulation process of compressed perceptual radar is proposed. The main work is as follows: (1) the high dimensional joint sparse representation model of MIMO radar space-time echo signal under pulse system is studied. By analyzing the slicing mode of three-dimensional data block of space-time echo signal, three methods of constructing multi-pulse slice of space-time echo signal with high dimension and sparsity dictionary are studied, and a sparsity optimal joint sparse representation model is designed. It provides the basis for MIMO radar space-time echo high-dimensional joint sparse reconstruction. (2) aiming at the problem of single target pulse group moving across the range unit, a new target range walk compensation method based on joint sparse dictionary pulse alignment is proposed. By aligning the combined sparse dictionary pulse groups, the correction target walks across the distance units, and the pulse accumulation is realized on the basis of the pulse accumulation within the group. Simulation results show that the proposed method improves the estimation performance of moving target parameters of compressed sensing radar under moving across range units. (3) Space-time high-dimensional combined sparse dictionary construction is studied respectively. And the application of target range walk compensation method based on joint sparse dictionary pulse group alignment to the motion target parameter estimation of centralized and distributed MIMO radar. The space-time high-dimensional joint sparse representation model of centralized and distributed MIMO radar and the corresponding joint sparse dictionary pulse alignment method are designed to realize the multi-parameter joint estimation of moving targets of centralized and distributed compressed MIMO radar at low SNR.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN958

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本文編號(hào)：2204905