發(fā)布時(shí)間：2018-07-05 02:35

  本文選題：MIMO雷達(dá) + 自適應(yīng)波形設(shè)計(jì)��； 參考：《哈爾濱工業(yè)大學(xué)》2016年博士論文

【摘要】：多輸入多輸出(Multiple input multiple output,MIMO)雷達(dá)已成為雷達(dá)信號(hào)處理領(lǐng)域中快速發(fā)展的一種新體制雷達(dá),最近十幾年受到了很多學(xué)者的關(guān)注。它的優(yōu)勢(shì)主要來(lái)自于其波形分集和靈活的陣列配置。隨著對(duì)MIMO雷達(dá)研究的深入,發(fā)射波形不再僅限于選擇正交波形,而是能夠根據(jù)實(shí)際需求靈活設(shè)計(jì)發(fā)射方向圖,將發(fā)射能量集中于感興趣的區(qū)域或者逼近期望的方向圖。這種由波形設(shè)計(jì)方向圖的方法一般被分為兩種:方向圖匹配或最小旁瓣設(shè)計(jì)。方向圖匹配一般不考慮峰值旁瓣過(guò)高的問(wèn)題。同時(shí),這種設(shè)計(jì)方向圖的波形設(shè)計(jì)通常也忽略了其它自適應(yīng)處理的要求,例如時(shí)域上抑制距離旁瓣和頻域上抗有源干擾的需求。子陣級(jí)陣列與稀疏陣列是MIMO雷達(dá)常用的兩種陣列配置。使用子陣級(jí)陣列的相控-MIMO雷達(dá)通常只考慮輸出信噪比的改善,但卻忽視了由于波形分集需要具備更高的抗干擾能力。MIMO雷達(dá)使用稀疏陣列可獲取額外的自由度,但由于其方向圖設(shè)計(jì)問(wèn)題的復(fù)雜性而增加了陣列優(yōu)化的難度,現(xiàn)有的算法很難滿(mǎn)足這樣的要求。本課題針對(duì)這些問(wèn)題和缺陷,著重研究了MIMO雷達(dá)的自適應(yīng)波形設(shè)計(jì)和陣列優(yōu)化,并提供新的解決方法。首先,針對(duì)方向圖匹配和抑制距離旁瓣干擾的兩種需求,提出了迭代對(duì)角逼近算法(Iterative Diagonal Approximation Algorithm,IDAA)和任意維迭代譜逼近算法框架(Arbitrary-Dimensional Iterative Spectral Approximation Algorithm,ADISAA),分別解決了方向圖匹配過(guò)程中旁瓣水平過(guò)高的問(wèn)題和兩種窄帶波形的約束條件同時(shí)優(yōu)化的問(wèn)題。在IDAA算法中,方向圖設(shè)計(jì)可被建模為期望方向圖與導(dǎo)向矢量逆變換的卷積,通過(guò)使用迭代對(duì)角逼近的方式實(shí)現(xiàn)與期望方向圖匹配的方向圖。在ADISAA算法框架中,除了IDAA之外,本文通過(guò)對(duì)Multi-WECAN(Multi-sequence weighted cyclic algorithm-new)算法進(jìn)行投影算子實(shí)現(xiàn),使其能夠作為其中一個(gè)子算法,由此設(shè)計(jì)指定區(qū)間相關(guān)旁瓣凹口抑制距離旁瓣干擾,進(jìn)而實(shí)現(xiàn)了包含兩種功能的窄帶恒模波形優(yōu)化。由此可見(jiàn),將IDAA子算法與ADISAA結(jié)合之后,該算法框架可被視為一種高效的面向方向匹配的波形設(shè)計(jì)框架,可兼容其它約束條件的同時(shí)優(yōu)化。仿真結(jié)果表明,相比現(xiàn)有的方法,ADISAA能夠犧牲一部分的方向圖匹配性能而獲取更深相關(guān)旁瓣的凹口深度,且具有靈活的調(diào)整能力,更能滿(mǎn)足MIMO雷達(dá)自適應(yīng)波形設(shè)計(jì)的需求。其次,針對(duì)方向圖匹配、抑制有源干擾和距離旁瓣干擾的三種需求,提出了一種多功能阻帶波形設(shè)計(jì)方法,即使用ADISAA算法框架設(shè)計(jì)一種滿(mǎn)足方向圖匹配,指定區(qū)間良好的相關(guān)特性和功率譜凹口的多功能阻帶波形�？紤]到常規(guī)寬帶波形的二維空間特性導(dǎo)致方向圖旁瓣水平過(guò)高的問(wèn)題,本文不對(duì)頻率以及帶寬進(jìn)行嚴(yán)格的約束,而將其作為一個(gè)待優(yōu)化變量,通過(guò)對(duì)秩虧傅里葉換算法進(jìn)行投影算子的推導(dǎo),將其作為ADISAA算法框架中的一個(gè)子算法,與方向圖匹配和指定區(qū)間良好相關(guān)特性等投影算子結(jié)合,實(shí)現(xiàn)了一種面向方向圖匹配的多功能阻帶波形優(yōu)化設(shè)計(jì)。第三,針對(duì)抗壓制性干擾和頻率正交信號(hào)優(yōu)化的需求,提出了一種極化波束形成的方法和基于迭代矩陣譜逼近算法(Iterative Matrix Spectral Approximation Algorithm,IMSAA)的波形優(yōu)化設(shè)計(jì)方法。本文考慮一種極化相控-MIMO雷達(dá),即子陣單元為極化陣列,通過(guò)對(duì)零點(diǎn)凹口進(jìn)行正交極化約束,有效地提高了對(duì)壓制性干擾的抑制能力;接著,對(duì)子陣單元之間的頻率分集信號(hào)的正交優(yōu)化問(wèn)題進(jìn)行建模,使用IMSAA算法設(shè)計(jì)相關(guān)性能良好的發(fā)射信號(hào)。仿真驗(yàn)證了極化波束形成方法和波形優(yōu)化算法的有效性。最后,針對(duì)MIMO雷達(dá)稀疏陣列優(yōu)化設(shè)計(jì)的需求,提出了一種基于多目標(biāo)差分進(jìn)化(multi-objective differential evolution,MODE)算法的稀疏陣列優(yōu)化方法,解決了方向圖匹配和峰值旁瓣水平抑制的并行優(yōu)化問(wèn)題。在第一個(gè)步驟中,選擇循環(huán)算法(cyclic algorithm,CA)設(shè)計(jì)滿(mǎn)足滿(mǎn)陣方向圖匹配的協(xié)方差矩陣;在第二個(gè)步驟中,根據(jù)前面得到的協(xié)方差矩陣,對(duì)面向方向圖匹配的稀疏陣列優(yōu)化問(wèn)題進(jìn)行數(shù)學(xué)建模,引進(jìn)遺傳算法(Genetic algorithm,GA)和差分進(jìn)化算法(Differential evolution,DE)求解該單目標(biāo)優(yōu)化問(wèn)題�？紤]到這兩種單目標(biāo)優(yōu)化算法無(wú)法對(duì)峰值旁瓣水平進(jìn)行抑制,將峰值旁瓣抑制作為一個(gè)不等約束條件,得到了一個(gè)約束優(yōu)化問(wèn)題,進(jìn)而轉(zhuǎn)為多目標(biāo)優(yōu)化問(wèn)題。針對(duì)此,本文使用MODE算法求解該多目標(biāo)優(yōu)化問(wèn)題。仿真結(jié)果表明:使用MODE算法能夠設(shè)計(jì)滿(mǎn)足方向圖匹配和峰值旁瓣抑制的稀疏陣列。
[Abstract]:Multiple input multiple output (MIMO) radar has become a new system radar in the field of radar signal processing, which has been paid much attention by many scholars in the last decade. Its advantages are mainly from its waveform diversity and flexible array configuration. With the in-depth study of MIMO radar, the waveform is not transmitted. It is only to select the orthogonal waveforms, but to design the emission pattern flexibly according to the actual needs, to focus the emission energy on the region of interest or to approximate the expected direction. This method of pattern design by the waveform is generally divided into two kinds: the pattern matching or the minimum sidelobe design. The pattern matching generally does not consider the peak side. At the same time, the design of the design pattern is usually ignored by other adaptive processing requirements, such as the demand for the suppression of the distance sidelobe and the active interference in the frequency domain in the time domain. Subarray and sparse arrays are two commonly used array configurations for MIMO radar. A phased -MIMO radar using a subarray array is usually used. Only considering the improvement of the output signal to noise ratio, but ignoring the need for higher anti-interference ability of the waveform diversity,.MIMO radar can obtain extra freedom by using sparse array. But because of the complexity of the design problem of its directional map, the difficulty of array optimization is increased. The existing algorithm is difficult to meet such requirements. These problems and defects are focused on the study of adaptive waveform design and array optimization of MIMO radar, and provide new solutions. Firstly, the iterative diagonal approximation algorithm (Iterative Diagonal Approximation Algorithm, IDAA) and arbitrary dimensional iterative spectral approximation are proposed for the two requirements of the direction map matching and the suppression of the distance sidelobe interference. The Arbitrary-Dimensional Iterative Spectral Approximation Algorithm (ADISAA) solves the problem of high sidelobe level in the direction matching process and the simultaneous optimization of the constraints of two narrow band waveforms. In the IDAA algorithm, the pattern design can be modeled as the volume of the volume of the expected direction map and the direction vector inverse change. In the ADISAA algorithm framework, this paper implements the projection operator of the Multi-WECAN (Multi-sequence weighted cyclic algorithm-new) algorithm so that it can be used as one of the sub algorithms to design the specified interval phase in the framework of the Multi-WECAN (Multi-sequence weighted cyclic algorithm-new) algorithm. The close sidelobe restrains the distance sidelobe interference and realizes the optimization of the narrow band constant mode waveform containing two functions. This can be seen that, after combining the IDAA subalgorithm with ADISAA, the algorithm framework can be considered as a highly efficient direction oriented waveform design framework, which can be compatible with its constraint conditions at the same time. Compared with the existing methods, ADISAA can obtain deeper correlation sidelobe depth of concave mouth at the expense of a part of the pattern matching performance, and has flexible adjustment ability, and can meet the needs of adaptive waveform design of MIMO radar. Secondly, three requirements for active interference and distance sidelobe interference are suppressed. The design method of the functional stopband waveform is to use the ADISAA algorithm framework to design a multi-function band waveform that satisfies the direction map matching, specifies the good correlation characteristics of the interval and the power spectrum concave. Considering the problem of the high level of the sidelobe in the direction map, the two-dimensional space characteristics of the conventional wide-band waveform will not be strict with the frequency and bandwidth. The constraint of lattice is used as a variable to be optimized. By deriving the projection operator from the rank deficiency Fourier transform algorithm, it is used as a sub algorithm in the framework of ADISAA algorithm. It combines with the projection operator, which is matched with the direction map and the good correlation characteristic of the specified interval, and realizes a multi-function band waveform optimization for the directional map matching. Third, in view of the demand for compression interference and frequency orthogonal signal optimization, a method of polarization beam formation and an optimization design method based on Iterative Matrix Spectral Approximation Algorithm (IMSAA) are proposed. A polarization phased -MIMO radar, the subarray unit, is considered in this paper. For the polarization array, the suppression ability to suppressing interference is effectively improved by orthogonal polarization constraint on the zero point notch. Then, the orthogonal optimization problem of the frequency diversity signal between the subarray units is modeled and the IMSAA algorithm is used to design the good correlation signal. The polarization beam forming method and wave are verified by simulation. In the end, a sparse array optimization method based on multi target differential evolution (multi-objective differential evolution, MODE) algorithm is proposed for MIMO radar sparse array optimization design, which solves the parallel optimization problem of directional map matching and peak side lobe suppression. The first step is the first step. In this case, cyclic algorithm (CA) is designed to satisfy the covariance matrix matching the full array direction map. In the second steps, the mathematical modeling of the sparse array optimization problem matching the opposite direction map is modeled according to the covariance matrix obtained before, and the genetic algorithm (Genetic algorithm, GA) and the differential evolution algorithm (Differential) are introduced. Evolution, DE) solves the single objective optimization problem. Considering that the two single objective optimization algorithms can not suppress the peak sidelobe level, the peak sidelobe suppression is regarded as a unequal constraint condition, and a constrained optimization problem is obtained, and then to the multi-objective optimization problem, the MODE algorithm is used to solve the multi-objective optimization problem. Simulation results show that the MODE algorithm can be used to design sparse arrays satisfying pattern matching and peak sidelobe suppression.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TN957.51

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2 鄧榮平;支持航空通信信號(hào)波形設(shè)計(jì)的頻譜檢測(cè)技術(shù)研究與實(shí)現(xiàn)[D];電子科技大學(xué);2015年

3 侯思堯;機(jī)載MIMO雷達(dá)正交波形設(shè)計(jì)及其檢測(cè)性能研究[D];電子科技大學(xué);2014年

4 陳翔;MIMO雷達(dá)波形設(shè)計(jì)與雜波仿真[D];電子科技大學(xué);2015年

5 王正南;干擾環(huán)境下的認(rèn)知雷達(dá)最優(yōu)波形設(shè)計(jì)[D];國(guó)防科學(xué)技術(shù)大學(xué);2013年

6 李少華;霍普金森壓桿試驗(yàn)中的兩種波形整形技術(shù)研究[D];東北大學(xué);2014年

7 林圣超;CAP可見(jiàn)光通信系統(tǒng)的波形設(shè)計(jì)與定時(shí)估計(jì)[D];東南大學(xué);2015年

8 李順宏;超寬帶無(wú)線電波形設(shè)計(jì)研究[D];大連大學(xué);2016年

9 孔凡堂;基于SQRT Chirp函數(shù)波形的多干擾抑制超寬帶系統(tǒng)設(shè)計(jì)[D];山東大學(xué);2016年

10 闞春秀;第五代移動(dòng)通信中的非正交波形技術(shù)研究[D];北京交通大學(xué);2016年

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