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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TAHMOUSH D. Review of micro-Doppler signatures[J]. IET Radar, Sonar & Navigation, 2015, 9(9): 1140–1146. doi: 10.1049/iet-rsn.2015.0118 CHEN V C, LI F, HO S S, et al. Micro-Doppler effect in radar: Phenomenon, model, and simulation study[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(1): 2–21. doi: 10.1109/TAES.2006.1603402 XIONG Xiangyu, LIU Hui, DENG Zhenmiao, et al. Micro-Doppler ambiguity resolution with variable shrinkage ratio based on time-delayed cross correlation processing for wideband radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4): 1906–1917. doi: 10.1109/TGRS.2018.2870149 张群, 胡健, 罗迎, 等. 微动目标雷达特征提取、成像与识别研究进展[J]. 雷达学报, 2018, 7(5): 531–547. doi: 10.12000/JR18049ZHANG Qun, HU Jian, LUO Ying, et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions[J]. Journal of Radars, 2018, 7(5): 531–547. doi: 10.12000/JR18049 HE Yongfu, PENG Yu, WANG Shaojun, et al. ADMOST: UAV flight data anomaly detection and mitigation via online subspace tracking[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68(4): 1035–1044. doi: 10.1109/TIM.2018.2863499 SEJDIĆ E, OROVIĆ I, and STANKOVIĆ S. Compressive sensing meets time-frequency: An overview of recent advances in time-frequency processing of sparse signals[J]. Digital Signal Processing, 2018, 77: 22–35. doi: 10.1016/j.dsp.2017.07.016 OH B S, GUO Xin, WAN Fangyuan, et al. Micro-Doppler mini-UAV classification using empirical-mode decomposition features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(2): 227–231. doi: 10.1109/LGRS.2017.2781711 SONG Chen, WU Yirong, ZHOU Liangjiang, et al. A multicomponent micro-Doppler signal decomposition and parameter estimation method for target recognition[J]. Science China Information Sciences, 2019, 62(2): 29304. doi: 10.1007/s11432-018-9491-y 章鹏飞, 李刚, 霍超颖, 等. 基于双雷达微动特征融合的无人机分类识别[J]. 雷达学报, 2018, 7(5): 557–564. doi: 10.12000/JR18061ZHANG Pengfei, LI Gang, HUO Chaoying, et al. Classification of drones based on micro-Doppler radar signatures using dual radar sensors[J]. Journal of Radars, 2018, 7(5): 557–564. doi: 10.12000/JR18061 REN Lingyun, TRAN N, FOROUGHIAN F, et al. Short-time state-space method for micro-Doppler identification of walking subject using UWB impulse Doppler radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2018, 66(7): 3521–3534. doi: 10.1109/TMTT.2018.2829523 ZHAO Yichao and SU Yi. Cyclostationary phase analysis on micro-Doppler parameters for radar-based small UAVs detection[J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(8): 2048–2057. doi: 10.1109/TIM.2018.2811256 MARÁK K, PETŐ T, BILICZ S, et al. Electromagnetic simulation of rotating propeller blades for radar detection purposes[J]. IEEE Transactions on Magnetics, 2018, 54(3): 7203504. doi: 10.1109/TMAG.2017.2752904 胡健, 罗迎, 张群, 等. 空间旋转目标窄带雷达干涉式三维成像与微动特征提取[J]. 电子与信息学报, 2019, 41(2): 270–277. doi: 10.11999/JEIT180372HU Jian, LUO Ying, ZHANG Qun, et al. Three-dimensional interferometric imaging and micro-motion feature extraction of rotating space targets based on narrowband radar[J]. Journal of Electronics &Information Technology, 2019, 41(2): 270–277. doi: 10.11999/JEIT180372 SPARR T and KRANE B. Micro-Doppler analysis of vibrating targets in SAR[J]. IEE Proceedings-Radar, Sonar and Navigation, 2003, 150(4): 277–283. doi: 10.1049/ip-rsn:20030697 STANKOVIC L, DAKOVIC M, THAYAPARAN T, et al. Inverse radon transform-based micro-Doppler analysis from a reduced set of observations[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 1155–1169. doi: 10.1109/TAES.2014.140098 ZHOU Yang, BI Daping, SHEN Aiguo, et al. Hough transform-based large micro-motion target detection and estimation in synthetic aperture radar[J]. IET Radar, Sonar & Navigation, 2019, 13(4): 558–565. doi: 10.1049/iet-rsn.2018.5407 CHEN Shiqian, YANG Yang, WEI Kexiang, et al. Time-varying frequency-modulated component extraction based on parameterized demodulation and singular value decomposition[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(2): 276–285. doi: 10.1109/TIM.2015.2494632 LI Ao, WU Zhiqiang, LU Huaiyin, et al. Collaborative self-regression method with nonlinear feature based on multi-task learning for image classification[J]. IEEE Access, 2018, 6: 43513–43525. doi: 10.1109/ACCESS.2018.2862159 STANKOVIC L, DAKOVIC M, and THAYAPARAN T. Time-Frequency Signal Analysis with Applications[M]. Norwood, USA: Artech House, 2013: 205–243. 李明, 吴娇娇, 左磊, 等. 基于实测数据的空中目标分类识别算法[J]. 电子与信息学报, 2018, 40(11): 2606–2613. doi: 10.11999/JEIT180024LI Ming, WU Jiaojiao, ZUO Lei, et al. Aircraft target classification and recognition algorithm based on measured data[J]. Journal of Electronics &Information Technology, 2018, 40(11): 2606–2613. doi: 10.11999/JEIT180024 YANG Yang, PENG Zhike, DONG Xingjian, et al. Application of parameterized time-frequency analysis on multicomponent frequency modulated signals[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(12): 3169–3180. doi: 10.1109/TIM.2014.2313961 罗迎, 龚逸帅, 陈怡君, 等. 基于跟踪脉冲的MIMO雷达多目标微动特征提取[J]. 雷达学报, 2018, 7(5): 575–584. doi: 10.12000/JR18035LUO Ying, GONG Yishuai, CHEN Yijun, et al. Multi-target micro-motion feature extraction based on tracking pulses in MIMO radar[J]. Journal of Radars, 2018, 7(5): 575–584. doi: 10.12000/JR18035 XU Huajian, YANG Zhiwei, TIAN Min, et al. An extended moving target detection approach for high-resolution multichannel SAR-GMTI systems based on enhanced shadow-aided decision[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 715–729. doi: 10.1109/TGRS.2017.2754098 -
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