High Speed Multi-target Parameter Estimation for FA-OFDM Radar Based on Expectation Maximization Algorithm
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摘要:
参数估计对雷达的目标检测和识别有着重要的意义。该文提出了一种基于期望最大化(EM)算法的捷变频联合正交频分复用(FA-OFDM)雷达高速多目标参数估计方法。首先，将窄带正交频分复用(OFDM)信号与传统捷变频雷达相结合，在每个脉冲宽度内同时发射多个载频随机跳变的子载波。然后，对单个脉冲内所有子载波的回波进行脉冲压缩和稀疏重构处理，得到1维高分辨距离。进一步地，将多个目标在不同脉冲时刻的高分辨距离信息构成观测数据，建立混合高斯模型。采用EM算法对模型参数和多个目标的距离、速度进行估计，并同时拟合多条时间-距离直线。直线斜率对应目标速度，直线纵轴截距对应目标初始距离。最终，分别分析了信噪比(SNR)对检测概率以及目标速度对相对估计误差的影响。仿真实验验证了所提算法的有效性。
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关键词:
- 捷变频联合正交频分复用雷达 /
- 参数估计 /
- 高速多目标 /
- EM算法
Abstract:Parameter estimation is very important for radar to detect and recognize targets. In this paper, a high speed multi-target parameter estimation method for Frequency Agility-Orthogonal Frequency Division Multiplexing(FA-OFDM) radar based on Expectation Maximization(EM) algorithm is proposed. Firstly, a promising idea is to combine narrowband Orthogonal Frequency Division Multiplexing (OFDM) signals and frequency agility, multiple subcarriers that frequency hopping randomly are simultaneously transmitted within each pulse width. Then, all echoes of a single pulse are compressed and sparsely reconstructed to achieve 1-demension high range resolution. Subsequently, the high resolution range of multiple targets at each pulse time are obtained to constitute the observation data, and Gauss mixture model is established. EM algorithm is applied to estimate the parameters of the model and the range and velocity of multiple targets. Also, multiple time-range lines are fitted at the same time, and the slope of the line corresponds to the velocity of the target, as well as, the vertical intercept of the line corresponds to the initial range of the target, separately. Finally, the influence of the Signal-to-Noise Ratio (SNR) on detection probability and the target velocity on relative error of estimation are analyzed, respectively. Simulations are provided to verify the effectiveness of the proposal.
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表 1 仿真参数
参数 数值 参数 数值 脉冲宽度 4 μs 脉冲重复频率 25 kHz 信号带宽 24 MHz 采样频率 48 MHz 子载波个数 64 中心载频 14 GHz 跳频总数 128 跳频带宽 20 MHz 脉冲总数 64 信噪比 –12 dB 目标距离 [3994,4001,4006] m 目标速度 [600,1220,5800] m/s 
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霍凯, 赵晶晶. OFDM新体制雷达研究现状与发展趋势[J]. 电子与信息学报, 2015, 37(11): 2776–2789. doi: 10.11999/JEIT150335HUO Kai and ZHAO Jingjing. The Development and prospect of the new OFDM radar[J]. Journal of Electronics &Information Technology, 2015, 37(11): 2776–2789. doi: 10.11999/JEIT150335 肖博, 霍凯, 刘永祥. 雷达通信一体化研究现状与发展趋势[J]. 电子与信息学报, 2019, 41(3): 739–750. doi: 10.11999/JEIT180515XIAO Bo, HUO Kai, and LIU Yongxiang. Development and prospect of radar and communication integration[J]. Journal of Electronics &Information Technology, 2019, 41(3): 739–750. doi: 10.11999/JEIT180515 WANG Jun, ZHANG Bocheng, and LEI Peng. Ambiguity function analysis for OFDM radar signals[C]. 2016 CIE International Conference on Radar, Guangzhou, China, 2016: 1–5. doi: 10.1109/RADAR.2016.8059592. 刘冰凡, 陈伯孝. 基于OFDM-LFM信号的MIMO雷达通信一体化信号共享设计研究[J]. 电子与信息学报, 2019, 41(4): 801–808. doi: 10.11999/JEIT180547LIU Bingfan and CHEN Baixiao. Integration of MIMO Radar and Communication with OFDM-LFM Signals[J]. Journal of Electronics &Information Technology, 2019, 41(4): 801–808. doi: 10.11999/JEIT180547 SCHWEIZER B, SCHINDLER D, KNILL C, et al. Expanding the unambiguous velocity limitation of the stepped-carrier OFDM radar scheme[C]. The 15th European Radar Conference, Madrid, Spain, 2018: 22–25. doi: 10.23919/EuRAD.2018.8546621. 黄瑞, 杜小勇, 胡卫东. OFDM雷达多目标运动参数的近似最大似然估计[J]. 雷达学报, 2018, 7(4): 507–513. doi: 10.12000/JR17116HUANG Rui, DU Xiaoyong, and HU Weidong. Approximate maximum likelihood estimator of multi-target motion parameters for orthogonal frequency division multiplexing radar[J]. Journal of Radars, 2018, 7(4): 507–513. doi: 10.12000/JR17116 LIU Yongjun, LIAO Guisheng, and YANG Zhiwei. Range and angle estimation for MIMO-OFDM integrated radar and communication systems[C]. 2016 CIE International Conference on Radar, Guangzhou, China, 2016: 1–4. doi: 10.1109/RADAR.2016.8059539. LELLOUCH G, TRAN P, PRIBIC R, et al. OFDM waveforms for frequency agility and opportunities for Doppler processing in radar[C]. 2008 IEEE Radar Conference, Rome, Italy, 2008: 1–6. doi: 10.1109/RADAR.2008.4720798. SCHWEIZER B, KNILL C, SCHINDLER D, et al. Stepped-Carrier OFDM-Radar processing scheme to retrieve high-resolution range-velocity profile at low sampling rate[J]. IEEE Transactions on Microwave Theory and Techniques, 2018, 66(3): 1610–1618. doi: 10.1109/TMTT.2017.2751463 LELLOUCH G, MISHRA A K, and INGGS M. Stepped OFDM radar technique to resolve range and Doppler simultaneously[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(2): 937–950. doi: 10.1109/TAES.2014.130753 KNILL C, SCHWEIZER B, ROOS F, et al. High range and Doppler resolution by application of compressed sensing using low baseband bandwidth OFDM radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2018, 66(7): 3535–3546. doi: 10.1109/TMTT.2018.2830389 LI Hongtao, WANG Chaoyu, WANG Ke, et al. High resolution range profile of compressive sensing radar with low computational complexity[J]. IET Radar, Sonar & Navigation, 2015, 9(8): 984–990. doi: 10.1049/iet-rsn.2014.0454 KNILL C, ROOS F, SCHWEIZER B, et al. Random multiplexing for an MIMO-OFDM radar with compressed sensing-based reconstruction[J]. IEEE Microwave and Wireless Components Letters, 2019, 29(4): 300–302. doi: 10.1109/LMWC.2019.2901405 QUAN Yinghui, LI Yachao, WU Yaojun, et al. Moving target detection for frequency agility radar by sparse reconstruction[J]. Review of Scientific Instruments, 2016, 87(9): 094703. doi: 10.1063/1.4962700 QUAN Yinghui, WU Yaojun, LI Yachao, et al. Range-Doppler reconstruction for frequency agile and PRF-jittering radar[J]. IET Radar, Sonar & Navigation, 2018, 12(3): 348–352. doi: 10.1049/iet-rsn.2017.0421 CHOI J. Sparse signal detection for space shift keying using the Monte Carlo EM algorithm[J]. IEEE Signal Processing Letters, 2016, 23(7): 974–978. doi: 10.1109/LSP.2016.2577707 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012: 162–165. -
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