基于ILVD的多分量LFM信号重构算法

针对线性调频(linear frequency modulated,LFM)信号参数估计技术面向多目标产生的多分量信号会产生交叉项,从而降低参数估计精度的问题,提出了基于逆吕氏分布 (inverse Lv’s distribution,ILVD)的多分量LFM信号重构算法。利用特征值分解结合最小误差准则方法的中心频率-调频斜率 (centroid frequency-chirp rate,CFCR)域信号分析与合成,克服传统时频变换方法中的交叉项干扰问题;采用特征值分解思想构造ILVD变换核函数;使用最小误差准则校正CFCR域随机相位误差,重构出分布在CFCR域吕氏分布中不同位置对应的各个目标信号。实验结果表明,提出的算法在重构信号的准确度和抗噪声干扰方面的有效性均有较大提高。

Multi-component LFM signal reconstruction algorithm based on ILVD

As an important technology in radar signal processing, the parameter estimation technology of linear frequency modulated (LFM) signal has been widely used in radar detection, radar imaging and other fields. However, most of the multi-component LFM signals generated by the existing LFM signal parameter estimation technologies for multi-target produce cross terms, reducing the accuracy of parameter estimation. Therefore, this paper proposed a multi-component LFM signal reconstruction algorithm based on inverse Lv’s distribution (ILVD) transform. This algorithm used eigenvalue decomposition combined with minimum error criterion to analyze and synthesize signals in the central frequency chirp rate (CFCR) domain, overcoming the cross-term interference problem in the traditional time-frequency transform method. At the same time, the idea of eigenvalue decomposition was used to construct ILVD transform kernel function. Finally, the minimum error criterion was used to correct the random phase error in the CFCR domain, and various target signals distributed at different positions in the Lv’s distribution of CFCR domain were reconstructed. Experimental results showed that the proposed algorithm was accurate in signal reconstruction and effective in anti-noise interference.