冲击噪声下基于混合LL_2-L_1优化求解的CSR参数估计  

Parameter Estimation for CSR under Impulsive Noise Based on Mixed LL_2-L_1 Optimization

作　　者：代林[1] 崔琛[1] 余剑[1] 梁浩[1] 

机构地区：[1]电子工程学院401教研室,合肥230037

出　　处：《现代雷达》2015年第6期26-31,共6页Modern Radar

基　　金：国家自然科学基金资助项目(60702015)

摘　　要：针对冲击噪声环境下压缩感知雷达参数估计性能急剧下降的问题,提出一种新的鲁棒性参数估计方法。首先,根据压缩感知雷达参数估计的稀疏线性模型,基于Lorentzian范数和L1范数稀疏正则化构造冲击噪声背景下稀疏重构的混合LL2-L1范数优化模型;然后,利用迭代加权最小二乘法和阈值收缩函数推导上述模型优化求解的一步迭代公式;最后,从理论上对文中算法的收敛性进行证明,并给出算法计算复杂度的定量分析。计算机仿真实验表明,文中算法在冲击噪声下支撑集的重构更精确、重构信号的精度更高、重构的计算量更小。In the presence of impulsive noise, the degeneracy of the robustness of most existing sparse recovery algorithms results in a sharp decline in the performance of parameter estimation for compressed sensing radar （CSR）. In this paper, a novel robust parameter estimation method used in impulsive noise environment-LTSIRLS （Lorentzian based Threshold-Shrinkaged IRLS） was proposed. Firstly, the mixed LL2-L1 optimization model for sparse recovery under impulsive noise environment was built based on Lorentzian constrained L1 regularization. Secondly, the iterative formulation was deduced by exploiting the IRLS （ Iteratively Reweighted Least Squares） and the threshold-shrinkage function. Thirdly, the convergence property of the proposed algorithm was theoretically proved and the computational complexity was quantitatively analyzed. It is verified that the proposed method results in more accurate support and signal recovery, smaller computational burden.

关 键 词：压缩感知雷达 冲击噪声 Lorentzian范数 优化求解 

分 类 号：TN957.51[电子电信—信号与信息处理]