Detection and estimation of moving targets based on fractional Fourier transform

Tao Ran, Ping Xianjun, Zhao Xinghao, Wang Yue
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引用次数: 8

Abstract

The key part of classical MTD is the filter bank based on FFT. Generally, a moving target moves in the way of segment constant acceleration. Thus, its echo can be approximately regarded as a chirp signal, which is a typical nonstationary signal. The traditional Fourier transform cannot handle chirp signals. Therefore, time frequency analysis is a more appropriate method than FFT in such an application. Furthermore, WVD have the best time frequency centralization performance among all the time frequency distributions. Instead of FFT, we can employ WVD to analyze the echo of moving targets. WVD for multiple chirp signals, however, produces inevitably some cross terms, which lead to the appearance of false targets. A method of radar moving target detection and estimation based on the fractional Fourier transform is proposed in this paper. Compared with the MTD based on WVD, the MTD based on FRFT need not consider the cross-term interference in the case of multi moving targets, and therefore reduces the computation burden, simplifies the processing procedure and improves the performance of detection. The experiment results verify validity of the method.
基于分数阶傅里叶变换的运动目标检测与估计
经典MTD的关键部分是基于FFT的滤波器组。一般情况下,运动目标以分段恒加速度运动。因此,它的回波可以近似地看作是一个啁啾信号,是一种典型的非平稳信号。传统的傅里叶变换不能处理啁啾信号。因此,在这种应用中,时频分析比FFT更合适。在所有时频分布中,WVD具有最好的时频集中性能。我们可以用WVD来代替FFT来分析运动目标的回波。然而,对于多个啁啾信号,WVD不可避免地会产生一些交叉项,从而导致假目标的出现。提出了一种基于分数阶傅里叶变换的雷达运动目标检测与估计方法。与基于WVD的MTD相比,基于FRFT的MTD在多运动目标情况下不需要考虑交叉项干扰,从而减少了计算量,简化了处理过程,提高了检测性能。实验结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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