基于稀疏表示的多雷达信号二维融合处理

Multi-radar Signal Two-dimensional Fusion Processing Based on Sparse Representation

多雷达信号融合通过对多视角和多频带雷达信号进行相干融合,可以提高图像的距离和方位向分辨率。为了克服基于谱估计的多雷达信号融合方法稳健性严重依赖于散射点个数估计精度和二维极点配对精度的问题,在深入研究逆合成孔径雷达(ISAR)信号的基础上,构造了多雷达信号二维融合的线性表示模型,将融合处理转化为一个信号表示问题;充分挖掘ISAR信号在傅里叶域的稀疏特性,提出了基于信号稀疏表示的多雷达信号融合方法。研究表明:基于信号稀疏表示的多雷达信号二维融合处理的参数估计精度优于谱估计方法,且运算效率略低于谱估计方法,但是参数估计性能受信号稀疏度的影响。

Using multi-radar signal fusion to fuse multi-angle and multi-band signals can improve the range and cross-range resolution of images.To overcome the limitation of multi-radar signal fusion based on spectral estimation,whose solidity depends heavily on the estimated accuracy of the number of scattering centers and the matching accuracy of two-dimensional poles,a linear representation model of multi-radar signal fusion is constructed.Then the sparsity of inverse synthetic aperture radar（ISAR） signal in the Fourier domain is excavated,and a new multi-radar signal fusion method based on signal sparse representation is proposed in this paper.From the analysis and simulation,it can be seen that the accuracy of parameters estimation based on signal sparse representation is better than that from the spectral estimation,while the operation efficiency of the new method is a little lower.But the performance of parameters estimation of the new method is affected by signal sparisty.