相控阵雷达低数据率微动目标时频特征提取方法

    Time-Frequency Feature Extraction Method for Low Data Rate Micro-Motion Targets of Phased Array Radar

    • 摘要: 面对多目标探测与跟踪场景,相控阵雷达数据率往往较低,高速平动和微运动导致了严重的多普勒模糊,传统方法无法进行时频特征提取。针对该问题,本文首先基于脉内干涉解模糊对微动目标的多普勒范围进行压缩,但该解模糊方法的多普勒缩小倍数最小为雷达信号载频与带宽的比值,窄带情况下,该缩小倍数足以使多普勒频率压缩到零频附近,导致时频特征难以获取;进一步采用参数化时频分析方法获得微动目标的精细时频特征;最后,进行微动周期估计,对比了本文所提方法与经典平均幅度差函数(Average Magnitude Difference Function, AMDF)方法进行周期估计的均方根误差。仿真结果表明,与传统方法相比,本文方法进行周期估计的均方根误差降低13.5dB。

       

      Abstract: In scenarios of multi-target detection and tracking, phased array radar data rates are often low, and high-speed translation and micro-motions lead to severe Doppler ambiguities, making it impossible for traditional methods to extract time-frequency features. Regarding this issue, this paper first compresses the Doppler range of micro-motion targets based on intra-pulse interference ambiguity resolution. However, the minimum Doppler compression factor of this ambiguity resolution method is the ratio of the radar signal carrier frequency to the bandwidth. In the case of narrowband signals, this compression factor is sufficient to compress the Doppler frequency near zero, making it difficult to obtain time-frequency characteristics; furthermore, a parametric time-frequency analysis method is used to obtain the fine time-frequency characteristics of micro-motion targets. Finally, a micro-motion period estimation was conducted, comparing the root mean square error of period estimation between the method proposed in this paper and the classical Average Magnitude Difference Function (AMDF) method. Simulation results show that compared with traditional methods, the root mean square error of period estimation using the method in this paper is reduced by 13.5 dB.

       

    /

    返回文章
    返回