【摘要】：基于時空兩維隨機輻射場的微波凝視關(guān)聯(lián)成像是一種全新的雷達(dá)成像體制,它可對固定區(qū)域進行凝視成像,同時可獲得突破天線孔徑限制的分辨率,具有重要的應(yīng)用價值。由于該成像體制中輻射場的時空兩維隨機特性,不同于傳統(tǒng)的成像方式,這里需將已知的輻射場和接收的散射回波做信息的關(guān)聯(lián)處理來獲得目標(biāo)圖像。關(guān)聯(lián)處理的方法有多類,如基于格拉姆-施密特(Gram-Schmidt)正交化的信息處理方法以及基于正則化的信息處理方法等。本文針對稀疏目標(biāo)場景,研究了基于壓縮感知(Compressive Sensing, CS)的關(guān)聯(lián)成像信息處理方法。 壓縮感知理論表明,若信號具有稀疏性,便可通過少數(shù)隨機測量的數(shù)據(jù),通過稀疏恢復(fù)算法重構(gòu)信號。本文研究的出發(fā)點正是利用目標(biāo)散射點分布的稀疏性這一先驗,通過稀疏重構(gòu)技術(shù)使系統(tǒng)獲得更好的成像效果。 論文首先研究了經(jīng)典稀疏恢復(fù)算法在關(guān)聯(lián)成像系統(tǒng)中的應(yīng)用。由基于時空兩維隨機輻射場的微波凝視關(guān)聯(lián)成像系統(tǒng)模型推導(dǎo)得到了系統(tǒng)中接收回波、測量矩陣與目標(biāo)后向散射系數(shù)的表達(dá)式以及相互關(guān)系,建立了關(guān)聯(lián)成像的數(shù)學(xué)模型,并將經(jīng)典的FOCUSS (Focal Underdetermined System Solver)、稀疏貝葉斯學(xué)習(xí)(Sparse Bayesian Learning, SBL)等稀疏恢復(fù)算法應(yīng)用于微波凝視關(guān)聯(lián)成像系統(tǒng),并通過仿真驗證了應(yīng)用稀疏恢復(fù)算法可獲得超分辨特性。 其次,研究了靜止目標(biāo)的輻射場矩陣的失配問題。當(dāng)目標(biāo)的強散射點與預(yù)先劃分的網(wǎng)格點位置存在偏差,便會在輻射場矩陣中引入“擾動”,導(dǎo)致原有算法失效。針對該問題,重新推導(dǎo)并建立了關(guān)聯(lián)成像系統(tǒng)中目標(biāo)位置存在擾動的情況下的回波模型,提出了一種改進的基于約束總體最小平方(constrained least squares,CTLS)的稀疏自適應(yīng)校正反演算法,仿真驗證了所提算法在散射點位置偏差引起輻射場矩陣擾動情況下可以明顯提高稀疏恢復(fù)算法的恢復(fù)精度并能實現(xiàn)目標(biāo)位置誤差的自校正。 最后,研究了運動目標(biāo)的關(guān)聯(lián)成像算法。推導(dǎo)并建立了目標(biāo)運動場景下的回波模型,對現(xiàn)有的針對運動目標(biāo)的成像算法進行了系統(tǒng)調(diào)研和分析,指出了現(xiàn)有方法存在的不足并提出了一種基于速度估計的運動目標(biāo)的稀疏恢復(fù)算法。通過迭代的方法交替求解運動速度和目標(biāo)反射系數(shù)。在每次迭代中,通過最小化加權(quán)Lp模來進行成像,同時通過最小化殘差來進行速度估計。仿真驗證了所提方法可同時獲得高分辨圖像和精確的速度估計結(jié)果。
[Abstract]:Microwave staring correlation imaging based on spatio-temporal two dimensional random radiation field is a new radar imaging system. It can perform staring imaging in fixed area and obtain resolution of antenna aperture limitation. It has important application value. Because of the spatio-temporal stochastic characteristics of the radiation field in the imaging system, which is different from the traditional imaging method, the known radiation field and the received scattering echo need to be associated with the information to obtain the target image. There are many kinds of association processing methods, such as information processing based on Gram-Schmidt orthogonalization and regularization based information processing. In this paper, we study the method of processing the correlation imaging information based on compressed perceptual (Compressive Sensing, CS) for sparse target scene. Compression sensing theory shows that if the signal is sparse, the signal can be reconstructed by sparse recovery algorithm through a small number of randomly measured data. The starting point of this paper is to make use of a priori the sparsity of the scattering point distribution of the target and to obtain a better imaging effect by the sparse reconstruction technique. Firstly, the application of the classical sparse restoration algorithm in the correlation imaging system is studied. Based on the model of microwave gaze correlation imaging system based on two dimensional random radiation field in time and space, the expressions of received echo, measurement matrix and the backscattering coefficient of target are derived, and the mathematical model of correlation imaging is established. The classical sparse restoration algorithm such as FOCUSS (Focal Underdetermined System Solver), sparse Bayesian learning (Sparse Bayesian Learning, SBL) is applied to the microwave gaze correlation imaging system. The simulation results show that the sparse restoration algorithm can obtain superresolution characteristics. Secondly, the mismatch of the radiation field matrix of the stationary target is studied. When there is a deviation between the strong scattering point of the target and the position of the pre-divided grid point, the "perturbation" will be introduced into the radiation field matrix, which will lead to the failure of the original algorithm. In order to solve this problem, the echo model of the target position in the correlation imaging system is rederived and established, and an improved sparse adaptive correction inversion algorithm based on constrained population least square (constrained least squares,CTLS) is proposed. The simulation results show that the proposed algorithm can significantly improve the recovery accuracy of the sparse recovery algorithm and achieve self-correction of the target position error under the condition of the radiation field matrix disturbance caused by the scattering point position deviation. Finally, the correlation imaging algorithm of moving targets is studied. The echo model of target moving scene is derived and established, and the existing imaging algorithms for moving target are investigated and analyzed systematically. The shortcomings of the existing methods are pointed out and a sparse restoration algorithm for moving targets based on velocity estimation is proposed. The iterative method is used to solve the motion velocity and target reflection coefficient alternately. In each iteration, the imaging is performed by minimizing the weighted Lp mode and the velocity estimation is performed by minimizing the residual error. Simulation results show that the proposed method can simultaneously obtain high resolution images and accurate velocity estimation results.
【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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本文編號：2329430