基于Group lasso的分布式MIMO雷达参数估计与能量优化

针对压缩感知算法在分布式MIMO雷达参数估计性能上易受噪声影响而出现伪峰、定位不准等问题，结合目标散射系数所满足块稀疏的前提条件，提出了一种基于Group lasso模型框架下压缩感知算法的参数估计。Group lasso作为一种块稀疏模型，可以有效解决感知算法在低SNR时参数估计性能差的问题，有效抑制了噪声对稀疏信号的破坏，其性能明显优于感知算法中常用的凸松弛CVX方法。此外针对MIMO雷达目标散射系数准确估计的前提，提出了基于线性规划的发射能量优化分配策略，此时系统模型转化为单发多收的MISO雷达，且集中发射功率于散射系数较大的路径。仿真结果表明，优化发射能量后的MISO雷达性能优势明显，尤其是当路径起伏较大时，性能尤为突出。仿真实验验证了理论分析的正确性和算法模型的有效性。 

Target parameter estimation and energy allocation of distributed MIMO radar based on Group lasso

We consider multiple-input multiple-output(MIMO) radar system with widely spaced antennas,whose parameters of estimation under compressed sensing algorithms are always influenced by false peak or wrong localization.A new algorithm for parameters estimation based on Group lasso is present,which utilizes second order core programming to improve the precision under low SNR,since the RCS of target meet with the block sparse.By contrasting with conventional CVX,the proposed algorithm gets better performance of estimation.In addition,by use of the estimator of reflectivity,one strategy of transmitting energy allocation is also proposed by transfer MIMO radar into MISO radar.At the same time,we would double the energy of one transmitter,which has the bigger value of RCS.MISO radar with energy allocation gains an advantage over MIMO radar by using parameter metric,especially in the case for RCS of target is fluctuant.The analytical and empirical results establish that the proposed algorithm is better than classical method for estimation in MIMO radar.Simulation results verify the usefulness of the proposed algorithm.