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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。
Multi-Radar Signal Fusion By coherently combining multi-view and multi-band radar signals, the distance and azimuth resolution of the image can be increased. In order to overcome the problem that the robustness of the multi-radar signal fusion method based on spectral estimation relies heavily on the estimation accuracy of the number of scattering points and the accuracy of the two-dimensional pole matching, based on the further study on the inverse synthetic aperture radar (ISAR) signal, The linear representation model of two-dimensional signal fusion is used to transform the fusion process into a signal representation problem. The sparse characteristics of ISAR signal in the Fourier domain are fully tapped and a multi-radar signal fusion method based on signal sparse representation is proposed. The results show that the accuracy of parameter estimation of multi-radar signal fusion based on signal sparse representation is better than that of spectral estimation, and the computational efficiency is slightly lower than that of spectral estimation. However, the performance of parameter estimation is affected by signal sparsity.