基于LU分解的稀疏目标定位算法

针对基于orth的稀疏目标定位算法中orth预处理会影响原信号的稀疏性的问题，该文提出一种基于LU分解的稀疏目标定位算法。该算法通过网格化感知区域把目标定位问题转化为压缩感知问题，并利用LU分解法对观测字典进行分解得到新的观测字典。该观测字典有效地满足了约束等距性条件，同时对观测值的预处理过程不影响原信号的稀疏性，从而有效地保证了算法的重建性能，提升了算法的定位精度。实验结果表明，基于LU分解的稀疏目标定位算法的性能远优于基于orth的稀疏目标定位算法，目标的定位精度得到了较大地提升。

Localization Algorithm of Sparse Targets Based on LU-decomposition

For the localization algorithm of sparse targets based on orth, the orth preprocessing would affect the sparsity of original signals. A novel localization algorithm of sparse targets based on LU-decomposition is proposed. It translates target localization into compressive sensing issue by using gridding method for sensing area, and then utilizes LU-decomposition to obtain a new observation dictionary, which satisfies effectively the restricted isometry property. Moreover, the sparsity of original signal can not be affected during the preprocessing of data observed, which will ensure the reconstruction performance and improve the localization accuracy. The experimental results show that, compared with the localization algorithm of sparse targets based on orth, the localization algorithm proposed have a much better performance, and the target localization accuracy is excellently improved.