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针对现有重构算法及其改进算法在压缩感知雷达(CSR)参数估计中存在的稳健性不强、适用性不广等问题,提出了一种适用于冲击噪声背景的鲁棒性算法——Lorentzian-ISL0(基于Lorentzian范数的改进光滑l0范数).建立CSR参数估计的稀疏线性模型,并基于Lorentzian范数和高斯函数稀疏正则化,构造冲击噪声下稳健的优化目标函数;修正优化目标函数的牛顿方向,并沿修正方向对估计值进行更新,直至收敛.仿真实验结果表明:与已有算法相比,本文方法计算复杂度更小,支撑集重构更精确,信号重构精度更高.
Aiming at the problems of low robustness and poor applicability of the existing reconstruction algorithms and their improved algorithms in the estimation of CSR parameters, a robust algorithm suitable for impact noise background is proposed. Lorentzian-ISL0 (improved smooth l0 norm based on Lorentzian norm) .The sparse linear model of CSR parameter estimation is established, and based on Lorentzian norm and sparse regularization of Gaussian function, a robust optimization objective function under impulsive noise is constructed. Modified optimization objective The newton direction of the function and the updating direction are updated until the convergence.The simulation results show that compared with the existing algorithms, the proposed method has less computational complexity, more accurate support set reconstruction and more accurate signal reconstruction high.