An Estimation Method of Micro-movement Parameters of UAV Based on The Concentration of Time-Frequency
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摘要:
无人机旋翼转动产生的微多普勒调制能够反映此类目标的微动特性，准确估计无人机旋翼长度、转动频率对于无人机的检测与识别具有重要意义。该文针对调频连续波体制雷达，提出一种基于时频集中度指标(CTF)的多旋翼无人机微动特征参数估计方法，推导了无人机旋翼微动特征参数与微多普勒分量信号参数之间的映射关系，在时频旋转域基于时频集中度指标，提高了各微动分量的区分度，相比于传统方法，提高了多分量微多普勒信号的参数估计精度，在低信噪比环境下也具有很好的鲁棒性。通过仿真和实际场景实验验证了方法的有效性。
Abstract:The micro-Doppler modulation generated by the rotor rotation of UAV can reflect the micro-movement characteristics of such targets. Accurately estimating the rotor length and rotation frequency of the UAV is of great significance for UAV detection and recognition. In this paper, a method for estimating micro-movement parameters of multi-rotor UAV based on Concentration of Time-Frequency (CTF) is proposed for FMCW radar system. The mapping relationship between dynamic parameters of UAV rotor and signal parameters of micro-Doppler component is deduced. Based on time-frequency concentration index in time-frequency rotation domain, the discrimination of micro-motion components is improved. Compared with the traditional methods, the proposed method can improve the accuracy of multi-component micro-Doppler parameter. Furthermore, it has good robustness in low SNR. The validity of the method is verified by simulation and field test.
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表 1 微多普勒信号参数估计方法的计算效率对比
方法 STFT-Hough WVD-Hough HHT GWT CTF 运算时间(s) 89.4203 91.4924 64.8427 57.2526 71.2180 
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表 2 多次实验微动目标参数估计结果
实验次数 分量序号 旋翼长度(cm) 初始角度(°) 旋翼转速(Hz) 1 1 12.58 24.2 90.90 2 12.59 75.6 83.33 2 1 12.61 48.5 104.58 2 12.63 147.1 95.25 3 1 12.57 5.4 75.54 2 12.55 126.1 71.22
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