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运动目标激光微多普勒效应平动补偿和微动参数估计

郭力仁 胡以华 董骁 李敏乐

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运动目标激光微多普勒效应平动补偿和微动参数估计

郭力仁, 胡以华, 董骁, 李敏乐

Translation compensation and micro-motion parameter estimation of laser micro-Doppler effect

Guo Li-Ren, Hu Yi-Hua, Dong Xiao, Li Min-Le
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  • 利用激光探测微多普勒效应可以精确估计微动参数,有利于实现目标的准确分类和精细识别.运动目标的微多普勒效应是一种由多项式相位信号模型与正弦调频模型组成的混合信号.对于这类混合信号中的微动参数估计目前还未提出有效的方法.对此,本文提出一种基于分数阶傅里叶变换(FrFT)的平动补偿方法,通过设计对FrFT参数域的带宽搜索方法,可以从混合信号中精确估计平动参数,实现平动和微动的分离;通过设计静态参数粒子滤波器,从补偿后的信号中准确估计了微动参数;针对静态参数模型,采用马尔可夫-蒙特卡罗方法增加粒子多样性,并利用累积残差定义新的粒子权重计算函数,保证了算法在对多维参数估计时的快速有效收敛,避免了参数分别估计时误差传递的影响.通过仿真分析对比和实验数据,验证了本文所提补偿和参数估计算法的有效性.
    Precise target identification is significant for commanding and identifying enemies. The micro-Doppler effect (MDE) can reflect the subtle movement characteristics of the targets, which provides a new way of detecting and recognizing the target. However, the current research mainly focuses on the micro-motion feature extraction and classification of the targets, which is not capable of identifying the targets of the same type. In fact, by accurately estimating the micro-motion parameters and combining sufficient prior knowledge, the target can be accurately identified. Compared with the microwave radar, the laser detected MDE has high sensitivity and precision in micro-motion parameter estimation. This is more conducive to realizing the accurate classification and fine identification of the targets. In real detection, the MDE always exists in the moving targets. This will generate a mixed echo signal modeled by the polynomial phase signal and sinusoidal frequency modulation (SFM) signal. So far, there have been no effective methods of estimating the micro-motion parameters in such mixed signals. In this regard, a set of translational motion compensation and micro-motion parameter estimation methods is proposed in this paper. A bandwidth searching method based on the fractional Fourier transform (FrFT) is presented to precisely estimate the translation parameters, which will be used to realize the compensation for the translational motion. The advanced particle filtering (PF) method using the static parameter model is designed for the micro-motion parameters in the remaining SFM term. Given the lack of particle diversity in static parameter PF, the Markov chain Monte Carlo sampling is employed, which also helps to improve the algorithm efficiency. Meanwhile, a new likelihood function in calculating the particle weights is designed by using the cumulative residual. With this improvement, the correct convergence under multi-dimensional parameter condition is guaranteed. The proposed method can avoid the influence from error transfer and achieve efficient and accurate estimation. Compared with the typical method that combines the time-frequency analysis and the polynomial fitting through the simulation, the proposed FrFT method is verified to have little computation complexity and high estimation accuracy, where the relative estimation errors of the translational parameters are kept at 0.64% and 0.45%, respectively. The waveform similarity of the SFM signal phase between the compensated signal and the real one indicates that the accuracy fully meets the requirement for accurate estimation of the micro-motion parameters. Further, the simulation result also shows the high efficiency of the improved PF algorithm. The convergence time consumed by the proposed algorithm is 0.353 s, while the traditional method needs 0.844 s. In the end, the comparison with the experimental data from the traditional inverse Radon transform shows the effectiveness and necessity of the proposed method. The research results are conducive to the accurate and rapid estimation of micro-motion parameters, which lays a foundation for the fine target recognition based on the MDE.
      通信作者: 胡以华, skl_hyh@163.com
    • 基金项目: 国家自然科学基金(批准号:61271353)资助的课题.
      Corresponding author: Hu Yi-Hua, skl_hyh@163.com
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61271353).
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  • [1]

    Chen V C 2011 The Micro-Doppler Effect in Radar (London: Artech House) p20

    [2]

    Zhang D H 2016 M. S. Thesis (Beijing: Institute of Technology) (in Chinese) [张德华 2016 硕士学位论文 (北京: 北京理工大学)]

    [3]

    Pawan S, Ahmad F, Amin M 2011 Signal Process. 6 1409

    [4]

    Yang W G, Qu W X, Zhang R Y 2016 J. Equip. Acad. 27 107 (in Chinese) [杨文革, 屈文星, 张若禹 2016 装备学院学报 27 107]

    [5]

    Gini F, Giannakis G B 1999 IEEE Trans. Signal Process. 47 363

    [6]

    Han X, Du L, Liu H W 2015 J. Electron. Inform. Technol. 37 961 (in Chinese) [韩勋, 杜兰, 刘宏伟 2015 电子与信息学报 37 961]

    [7]

    Hong L, Dai F, Wang X 2016 IEEE Geosci. Remote Sens. Lett. 13 1349

    [8]

    Simeunovic M, Popovic-Bugarin V, Djurovic I 2017 IEEE Trans. Aerosp. Electron. Syst. 53 1273

    [9]

    Du L, Li L, Wang B 2016 IEEE Sens. J. 16 3756

    [10]

    Shu C Y, Huang P L, Ji J Z 2016 Syst. Engin. Electron. 38 259 (in Chinese) [束长勇, 黄沛霖, 姬金祖 2016 系统工程与电子技术 38 259]

    [11]

    Li K L 2010 Ph. D. Dissertation (Changsha: National University of Defense Technology) (in Chinese) [李康乐 2010 博士学位论文 (长沙: 国防科学技术大学)]

    [12]

    Wang Z F, Wang Y, Xu L 2017 IEEE Signal Proc. Lett. 24 1238

    [13]

    Yang Y C, Tong N N, Feng C Q 2013 Sci. Sin.: Inform. 43 1172 (in Chinese) [杨有春, 童宁宁, 冯存前 2013 中国科学: 信息科学 43 1172]

    [14]

    Zhang S N, Zhao H C, Xiong G 2014 Acta Phys. Sin. 63 158401 (in Chinese) [张淑宁, 赵惠昌, 熊刚 2014 物理学报 63 158401]

    [15]

    Zhu H, Zhang S N, Zhao H C 2015 Digital Signal Process. 40 224

    [16]

    Guo L R, Hu Y H, Wang Y P 2016 Proc. SPIE 10021 100211Z

    [17]

    Hu Y, Guo L, Dong X 2016 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS) Shanghai, China, November 2-4, 2016 p264

    [18]

    Zhu H, Zhang S N, Zhao H C 2014 Acta Phys. Sin. 63 058401 (in Chinese) [朱航, 张淑宁, 赵惠昌 2014 物理学报 63 058401]

    [19]

    Zhang W P 2014 M. S. Thesis (Changsha: National University of Defense Technology) (in Chinese) [张文鹏 2014硕士学位论文 (长沙: 国防科技大学)]

    [20]

    Lu W L, Xie J W, Wang H M, Sheng C 2016 Acta Phys. Sin. 65 080202 (in Chinese) [路文龙, 谢军伟, 王和明, 盛川 2016 物理学报 65 080202]

    [21]

    Zhu H, Zhang S 2013 Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC) Shengyang, China, December 20-22, 2013 p908

    [22]

    Gelman A, Carlin J B, Stern H S 2012 Bayesian Data Analysis (3rd Ed.) (Boca Raton: CRC Press) p106

    [23]

    Jouin M, Gouriveau R, Hissel D 2016 Mech. Syst. Signal Proc. 72 2

    [24]

    Wang C, Xu C F, Feng Q 2017 J. Beijing Institute Technol. 37 318 (in Chinese) [王才, 徐成发, 冯祺 2017 北京理工大学学报 37 318]

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出版历程
  • 收稿日期:  2017-12-28
  • 修回日期:  2018-04-26
  • 刊出日期:  2018-08-05

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