基于压缩感知的米波雷达低空测角算法

发布时间：2019-01-02 08:54

【摘要】：为解决多径环境下米波雷达对低空目标探测问题,本文结合信号空域稀疏性和多径模型下的复合导向矢量提出了一种多径环境下高分辨的低空测角方法,能够有效地克服多径效应问题。该方法首先利用多径模型下的先验信息产生复合导向矢量,然后利用该导向矢量构造压缩感知矩阵,此时的感知矩阵是整合了多径衰减系数和回波角度关系等先验信息,同时通过对多快拍数据矩阵的奇异值分解获得较高信噪比的信号数据矩阵,继而利用感知矩阵和信号数据矩阵建立最优化L1范数约束求解模型,最后利用凸优化工具求解稀疏空间谱,估计直达波和反射波入射角度值。该方法能够增强信息矢量稀疏性,在较低信噪比下可获得高分辨的角度估计性能。仿真实验证明了该方法的优越性。
[Abstract]:In order to solve the problem of low altitude target detection by Meter-wave radar in multi-path environment, a high-resolution low-altitude angle measurement method in multipath environment is proposed in this paper, which combines the spatial sparsity of signal and the composite guidance vector under multipath model. The problem of multipath effect can be overcome effectively. In this method, the priori information of multipath model is first used to generate the composite guidance vector, and then the compressed perception matrix is constructed by the guidance vector. In this case, the sensing matrix integrates the prior information such as the multipath attenuation coefficient and the echo angle relationship. At the same time, the signal data matrix with high signal-to-noise ratio is obtained by the singular value decomposition of the multi-beat data matrix, and then the optimal L1 norm constraint solution model is established by using the perceptual matrix and the signal data matrix. Finally, the sparse spatial spectrum is solved by convex optimization method, and the incidence angle of direct and reflected waves is estimated. This method can enhance the sparsity of information vector and obtain high resolution angle estimation performance at low SNR. The simulation results show the superiority of this method.
【作者单位】：西安电子科技大学雷达信号处理国防科技重点实验室;
【基金】：国家自然科学基金(61101244)资助课题
【分类号】：TN957.51

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