基于压缩感知的MIMO雷达多维目标参数估计方法研究

发布时间：2018-03-27 16:07

本文选题：压缩感知　切入点：MIMO雷达　出处：《南京信息工程大学》2017年硕士论文

【摘要】：MIMO雷达作为一种新体制雷达,与传统的相控阵雷达相比,在目标检测、波束形成和参数估计等方面具有明显的优势。在实际MIMO雷达应用中,目标往往只占据少数的分辨单元,即MIMO雷达的目标回波信号是稀疏的。因此,压缩感知理论能应用于MIMO雷达的目标参数估计问题中。本文设计了一种抗噪声能力强的自适应正则化SL0算法,以及研究MIMO雷达在病态感知矩阵和阵元失效条件下的多维目标参数估计问题,主要内容如下:(1)针对快速稀疏重构算法—SL0算法的抗噪声能力和稳健性较差的问题,提出一种自适应正则化的SL0算法。该算法在SL0算法的内循环最速上升法中以第一次迭代的信号残差项估计值以及该迭代前后的稀疏信号估计的偏差值作为当前正则化参数的选择依据,从而能自适应地调整在外循环迭代中的信号稀疏度和误差容许项的权重值,在优化过程中保持两者的平衡性,从而有效降低稀疏信号的重构误差,提高了 SL0算法的抗噪声干扰能力。(2)针对MIMO雷达因感知矩阵病态而导致SL0算法失效的问题,利用修正截断奇异值分解方法改善MIMO雷达的病态感知矩阵,使得SL0算法能有效应用于MIMO雷达的快速多目标参数估计。为了方便科研人员测试MIMO雷达的目标参数估计性能,开发了基于LabVIEW的病态感知矩阵下MIMO雷达目标参数估计测试软件。(3)为了解决MIMO雷达因阵元失效而导致其目标参数估计性能下降的问题,将矩阵填充应用于阵元失效MIMO雷达的目标参数估计。在失效阵元输出的整行或整列零元素上叠加微小的服从高斯分布的随机扰动量,使其能满足矩阵填充条件,并利用矩阵填充和迭代加权lq方法获得目标场景向量粗估计值,然后根据目标场景向量粗估计值和感知矩阵重构出失效阵元的目标接收数据,从而相比于未利用矩阵填充的重构算法,能以较高精度估计出目标的三维参数。
[Abstract]:As a new type of radar, MIMO radar has obvious advantages in target detection, beamforming and parameter estimation, compared with traditional phased array radar. In practical applications of MIMO radar, the target usually occupies only a few resolution units. That is, the target echo signal of MIMO radar is sparse. Therefore, compression sensing theory can be applied to the estimation of target parameters in MIMO radar. In this paper, an adaptive regularization SL0 algorithm with strong anti-noise ability is designed. And the multi-dimension target parameter estimation problem of MIMO radar under the condition of ill-conditioned perception matrix and array element failure is studied. The main contents are as follows: (1) aiming at the problem of poor anti-noise ability and robustness of the fast sparse reconstruction algorithm -SL0 algorithm, An adaptive regularization SL0 algorithm is proposed, in which the estimation of the signal residual of the first iteration and the deviation of the sparse signal before and after the first iteration are taken as the current positive values in the SL0 algorithm. The basis for the selection of chemical parameters, Thus, the signal sparsity and the weight of the error tolerance can be adjusted adaptively in the outer loop iteration, and the balance between the two can be maintained in the optimization process, thus effectively reducing the reconstruction error of the sparse signal. In order to solve the problem of SL0 algorithm failure caused by ill-conditioned perception matrix of MIMO radar, the modified truncated singular value decomposition (SVD) method is used to improve the ill-conditioned perceptual matrix of MIMO radar. The SL0 algorithm can be applied to the fast multi-target parameter estimation of MIMO radar effectively. In order to facilitate the researchers to test the performance of MIMO radar target parameter estimation, In order to solve the problem that the target parameter estimation performance of MIMO radar is degraded due to the failure of array elements, a testing software of MIMO radar target parameter estimation based on LabVIEW ill-conditioned perception matrix is developed in order to solve the problem that the performance of MIMO radar target parameter estimation is degraded due to the failure of array elements. The matrix filling is applied to estimate the target parameters of the array element failure MIMO radar. The random perturbation quantity distributed from Gao Si is superimposed on the whole line or whole column zero element of the invalid array element, so that the matrix filling condition can be satisfied. The rough estimate of target scene vector is obtained by filling matrix and iterative weighted LQ method, then the target receiving data of invalid matrix element is reconstructed according to the coarse estimation value of target scene vector and perception matrix. Compared with the reconstruction algorithm without matrix filling, the 3D parameters of the target can be estimated with high accuracy.
【学位授予单位】：南京信息工程大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN958

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