發(fā)布時(shí)間：2018-01-22 18:44

  本文關(guān)鍵詞： MIMO雷達(dá) 稀疏成像 正交匹配追蹤算法 觀測(cè)矩陣失配 擾動(dòng)矩陣 相位誤差 載頻偏差 網(wǎng)格失配　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：MIMO(Multiple input Multiple output,MIMO)雷達(dá)是指利用多個(gè)發(fā)射和接收天線同時(shí)對(duì)目標(biāo)進(jìn)行觀測(cè)的一種新構(gòu)型的雷達(dá)系統(tǒng)。陣列構(gòu)型設(shè)計(jì)和波形分集技術(shù)使MIMO雷達(dá)能夠獲得遠(yuǎn)多于實(shí)際物理陣元數(shù)目的觀測(cè)通道和空間自由度。通過(guò)對(duì)觀測(cè)通道回波的聯(lián)合處理,相比于傳統(tǒng)成像雷達(dá),MIMO雷達(dá)在成像的方位向分辨率、實(shí)時(shí)性和運(yùn)動(dòng)補(bǔ)償?shù)确矫嬗忻黠@的性能優(yōu)勢(shì)。進(jìn)一步的,為克服信號(hào)帶寬和系統(tǒng)采樣頻率在實(shí)現(xiàn)高分辨率成像時(shí)對(duì)雷達(dá)系統(tǒng)設(shè)計(jì)和實(shí)現(xiàn)的困難和限制,基于壓縮感知(Compressed Sensing, CS)的MIMO雷達(dá)稀疏成像開(kāi)始受到廣泛的關(guān)注,是當(dāng)前的一個(gè)研究熱點(diǎn)。由CS理論可知,MIMO雷達(dá)的稀疏重構(gòu)(即,反演)性能依賴于觀測(cè)矩陣的性質(zhì),因此一個(gè)精確已知的觀測(cè)矩陣是獲得好的反演結(jié)果的前提條件。眾所周知,MIMO雷達(dá)的觀測(cè)矩陣由雷達(dá)系統(tǒng)參數(shù)和成像場(chǎng)景的網(wǎng)格點(diǎn)共同決定,如果其中任一的一個(gè)因素存在不確定性都將導(dǎo)致實(shí)際觀測(cè)矩陣不再與默認(rèn)的觀測(cè)矩陣一致,這種觀測(cè)矩陣的失配必然對(duì)成像算法的有效性、可靠性和穩(wěn)健性提出了挑戰(zhàn)。因此,研究觀測(cè)矩陣失配對(duì)MIMO雷達(dá)稀疏成像的影響是有實(shí)際應(yīng)用意義的。 本文采用正交匹配追蹤算法(Orthogonal Matching Pursuit,OMP)作為反演算法的比較基準(zhǔn),圍繞系統(tǒng)參數(shù)和成像場(chǎng)景網(wǎng)格點(diǎn)這兩類因素的不確定性,重點(diǎn)研究和分析觀測(cè)矩陣失配的產(chǎn)生機(jī)理、OMP算法在實(shí)現(xiàn)有效反演時(shí)對(duì)這些不確定性的承受能力、以及高效重構(gòu)算法等問(wèn)題,主要的研究?jī)?nèi)容如下： 1、針對(duì)相位分集和頻率分集兩種波形分集方式,建立了對(duì)應(yīng)緊湊式MIMO雷達(dá)系統(tǒng)的回波模型,分別從點(diǎn)擴(kuò)散函數(shù)和空間譜的角度推導(dǎo)了成像分辨率和無(wú)模糊距離的解析表達(dá)式,重點(diǎn)分析了兩種角度下對(duì)成像分辨率描述的差異。詳細(xì)介紹了OMP算法的算法流程和基于互相關(guān)系數(shù)的重構(gòu)性能推導(dǎo)過(guò)程。同時(shí),根據(jù)互相關(guān)系數(shù)和點(diǎn)擴(kuò)散函數(shù)之間的緊密聯(lián)系,確定了通過(guò)點(diǎn)擴(kuò)散函數(shù)來(lái)分析觀測(cè)矩陣失配和稀疏反演性能的可行性。 2、對(duì)于系統(tǒng)可能存在的發(fā)射-接收通道隨機(jī)相位誤差,基于其在回波相位中不與散射點(diǎn)坐標(biāo)信息耦合的先驗(yàn)假設(shè),在MIMO雷達(dá)系統(tǒng)中建立了含有相位不確定性的回波模型,分析了這一類隨機(jī)相位誤差對(duì)觀測(cè)矩陣的作用形式,表現(xiàn)為一左乘對(duì)角擾動(dòng)矩陣。進(jìn)一步的,利用受擾動(dòng)的點(diǎn)擴(kuò)散函數(shù)和相位誤差的隨機(jī)特性,分析了左乘擾動(dòng)矩陣對(duì)OMP算法成像的影響,主要表現(xiàn)為幅度衰減且衰減程度由相位的波動(dòng)范圍決定。特別地,根據(jù)推導(dǎo)的OMP算法重構(gòu)性能,分別在支撐集恢復(fù)和幅值估計(jì)兩方面推導(dǎo)了OMP算法對(duì)相位誤差的容限�？紤]到回波中隨機(jī)相位誤差是一隱含變量的事實(shí),引入期望最大化(Expectation Maximization, EM)方法,根據(jù)最大后驗(yàn)概率準(zhǔn)則,提出了期望最大化的稀疏成像算法(Sparse Imaging via EM, SIEM),仿真結(jié)果顯示在存在相位誤差時(shí)SIEM比OMP具有更穩(wěn)定的反演性能。 3、對(duì)于系統(tǒng)可能存在的發(fā)射一接收通道載頻偏差,在相位分集MIMO雷達(dá)系統(tǒng)中建立了含有發(fā)射、接收載頻不確定性的解析回波模型,回波表達(dá)式表明載頻偏差不僅在回波相位中與散射點(diǎn)位置信息強(qiáng)耦合,而且會(huì)影響通道分離的性能,導(dǎo)致通道分離殘差的出現(xiàn)。相比隨機(jī)相位誤差,載頻偏差引起更加復(fù)雜、嚴(yán)重的觀測(cè)矩陣失配。根據(jù)受擾動(dòng)點(diǎn)擴(kuò)散函數(shù)的峰值變化,分析得到了載頻偏差對(duì)OMP算法成像的影響集中表現(xiàn)為對(duì)點(diǎn)擴(kuò)散函數(shù)峰值的衰減,然后進(jìn)一步推導(dǎo)了存在載頻偏差時(shí)OMP算法的反演性能變化以及OMP算法支撐集恢復(fù)和幅值估計(jì)對(duì)載頻偏差的容限。通過(guò)將載頻偏差引起的觀測(cè)矩陣失配表示為一個(gè)具有有界Frobenius范數(shù)約束的加性擾動(dòng)矩陣,提出了基于有界擾動(dòng)的稀疏成像算法(Sparse Imaging based on Frobenius-nrom-bounded Perturbation, SIFrobP)。根據(jù)有界擾動(dòng)的一般性假設(shè),SIFrobP算法的適用范圍廣泛,可適用于實(shí)際觀測(cè)矩陣中存在任意未知不確定性的場(chǎng)景。 4、研究了連續(xù)成像場(chǎng)景的離散化網(wǎng)格與真實(shí)目標(biāo)散射點(diǎn)之間存在不確定性時(shí)的網(wǎng)格失配問(wèn)題。從細(xì)化網(wǎng)格提高散射點(diǎn)位置估計(jì)精度的角度,將基于Band-exclusion技術(shù)的改進(jìn)型OMP算法(Band-excluded OMP,BOMP)引入MIMO雷達(dá)稀疏成像,利用點(diǎn)擴(kuò)散函數(shù)指導(dǎo)相關(guān)帶門限值的設(shè)置使BOMP算法成像的低分辨率得到了有效地改善。同時(shí),從摒棄對(duì)連續(xù)成像場(chǎng)景網(wǎng)格化的角度出發(fā),提出了基于連續(xù)參數(shù)估計(jì)的MIMO雷達(dá)稀疏成像方法(Sparse Imaging via Continuous Parameter Estimate,SICPE),推導(dǎo)了算法的性能條件。該算法不僅避免了經(jīng)典稀疏重構(gòu)算法對(duì)網(wǎng)格的依賴性,而且可以在發(fā)射／接收端稀疏布陣或非均勻采樣時(shí)均獲得較好的稀疏成像結(jié)果。
[Abstract]:MIMO (Multiple input Multiple output, MIMO) radar refers to a new type of radar system using multiple transmit and receive antennas at the same time to observe the target. The array configuration design and waveform diversity MIMO radar can obtain much more than the actual number of the array observation channel and spatial degrees of freedom. The combined treatment of the observation channel echo, compared to the traditional imaging radar, MIMO radar resolution in the azimuth, has obvious advantages in real-time and motion compensation. Further, in order to overcome the bandwidth of the signal and system sampling frequency in the realization of high resolution imaging radar system to design and realize the difficulties and limitations, based on compression perception (Compressed Sensing, CS) MIMO radar sparse imaging began to receive widespread concern. It is a research hotspot. According to the CS theory, the sparse MIMO radar Structure (i.e., inversion) performance depends on the nature of the observation matrix, so a precisely known observation matrix is a prerequisite for a good inversion result. As everyone knows, the observation matrix of MIMO radar is determined by the radar system parameters and imaging scene of grid points, if a factor in the existence of any uncertainty will cause the actual observation matrix and observation matrix is no longer consistent by default, this observation matrix mismatch and necessity of imaging algorithm, challenges the reliability and robustness. Therefore, research on the observation matrix is has practical significance of the effect of mismatch between MIMO radar sparse imaging.
This paper uses orthogonal matching pursuit algorithm (Orthogonal Matching Pursuit, OMP) as the benchmark inversion algorithm, surrounding the two kinds of factors of system parameters and the imaging scene grid point uncertainty, focuses on the research and analysis of the observation matrix mismatch mechanism, OMP algorithm to achieve effective inversion on these uncertainty capacity problems and efficient reconstruction algorithm, the main research contents are as follows:
1, considering the phase and frequency diversity of two kinds of waveform diversity, established the corresponding echo model of compact MIMO radar system, which spread function and spatial spectrum angle is deduced analytic expressions of imaging resolution and unambiguous distance from the point, the paper introduced two kinds of description on the imaging resolution angle of the differences in detail. Introduces the OMP algorithm and the reconstruction performance based on derivation of correlation coefficient. At the same time, according to the cross-correlation coefficient and point spread function between close contact, determine the feasibility analysis to the observation matrix mismatch and sparse inversion performance by point spread function.
2, the system may exist for transmitting and receiving channel random phase error, based on the echo phase in scattering and coordinate information coupling hypothesis in MIMO radar system, established a model containing echo phase uncertainty, analyzed this kind of random phase error function to form the observation matrix, performance as a left multiplication diagonal perturbation matrix. Further, based on the characteristics of random disturbance point spread function and phase error, analyzes the impact of the matrix on the left by the disturbance OMP imaging algorithm, mainly for the amplitude attenuation and the attenuation degree is determined by the phase fluctuation. Especially, according to the OMP algorithm performance is. Set recovery and amplitude estimation two derived tolerance OMP algorithm for phase error in support. Considering the random phase error is a latent variable echo in fact, into the expectation maximization (Ex Pectation Maximization (EM) method, according to the maximum a posteriori criterion, proposes the expectation maximization sparse imaging algorithm (Sparse Imaging via EM, SIEM). The simulation results show that SIEM has more stable performance than OMP in the presence of phase error.
3, the system may exist for the launch of a receiving channel in the carrier frequency offset, phase diversity MIMO radar system is established with transmitting, receiving and parsing the echo model of uncertainty of carrier frequency, carrier frequency offset echo expression shows that not only in the echo phase and scattering point position information and strong coupling, and will affect the performance of channel separation, resulting in channel the separation of residuals. Compared with the random phase error, carrier frequency deviation caused by the more complex, the observation matrix serious mismatch. According to the peak point spread function disturbance changes, analysis of the performance effect of carrier frequency offset on OMP algorithm for imaging attenuation of the PSF peak, then the carrier frequency offset are deduced when the change of inversion the performance of the OMP algorithm and OMP algorithm support recovery and amplitude estimation of carrier frequency deviation tolerance. The observation matrix caused by carrier frequency offset The mismatch is expressed as a perturbation matrix with additive bounded Frobenius norm constraint, is proposed based on sparse imaging algorithm of bounded disturbances (Sparse Imaging based on Frobenius-nrom-bounded Perturbation, SIFrobP). According to the general assumption of bounded disturbances, the scope of SIFrobP algorithm is widely applicable to the actual observation matrix in the presence of arbitrary the uncertainty in the scene.
4, there is mismatch between grid uncertainty on continuous imaging scene discretization grid and real target scattering points. From the angle of grid refinement to improve the estimation accuracy of scattering points, based on improved OMP algorithm based on Band-exclusion Technology (Band-excluded OMP BOMP) into MIMO radar imaging using sparse, point spread function guide with threshold value setting to effectively improve the low resolution BOMP imaging algorithm. At the same time, starting from the abandon of continuous imaging scene grid point of view, put forward the MIMO radar imaging method for the sparse parameter estimation based on (Sparse Imaging via Continuous Parameter Estimate, SICPE), derived the performance conditions of the algorithm. This method not only avoids the dependence on the grid of the classic sparse reconstruction algorithm, but also in the transmitting and receiving end or sparse array non uniform sampling are Better sparse imaging results were obtained.

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN958

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本文編號(hào)：1455474