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基于代价参考粒子滤波器组的检测前跟踪算法

卢锦 王鑫

卢锦, 王鑫. 基于代价参考粒子滤波器组的检测前跟踪算法[J]. 电子与信息学报, 2021, 43(10): 2815-2823. doi: 10.11999/JEIT210234
引用本文: 卢锦, 王鑫. 基于代价参考粒子滤波器组的检测前跟踪算法[J]. 电子与信息学报, 2021, 43(10): 2815-2823. doi: 10.11999/JEIT210234
Jin LU, Xin WANG. Cost-reference Particle Filter Bank Based Track-before-detecting Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2815-2823. doi: 10.11999/JEIT210234
Citation: Jin LU, Xin WANG. Cost-reference Particle Filter Bank Based Track-before-detecting Algorithm[J]. Journal of Electronics & Information Technology, 2021, 43(10): 2815-2823. doi: 10.11999/JEIT210234

基于代价参考粒子滤波器组的检测前跟踪算法

doi: 10.11999/JEIT210234
基金项目: 国家自然科学基金(61801281),陕西省教育厅专项科研计划(17JK0084)
详细信息
    作者简介:

    卢锦:女,1984年生,讲师,研究方向为雷达目标检测与状态估计

    王鑫:女,1979年生,副教授,研究方向为通信系统及信息安全

    通讯作者:

    卢锦 lj491216@163.com

  • 中图分类号: TN911.72; TN951

Cost-reference Particle Filter Bank Based Track-before-detecting Algorithm

Funds: The National Natural Science Foundation of China (61801281), The Scientific Research Project of Shaanxi Provincial Department of Education (17JK0084)
  • 摘要: 基于粒子滤波的检测前跟踪方法是检测和估计非线性调频信号的有效方法之一。但此类方法运算量大,难以并行执行。此外,由于粒子滤波算法收敛较慢,基于粒子滤波的检测前跟踪方法的检测和状态估计能力有待提高。针对上述问题,该文首先提出一种代价参考粒子滤波器组。该滤波器组收敛快速,具有完全的并行结构,可快速准确地估计非线性调频信号的瞬时频率。其次,提出基于代价参考滤波器组的检测前跟踪算法,可在给定虚警率下,在各个时刻检测目标和估计目标状态。两类非线性调频信号检测和估计的仿真结果表明,基于代价参考粒子滤波器组的检测前跟踪算法的检测性能、估计性能和运行速率均优于类似的方法,如基于粒子滤波的检测前跟踪方法,基于Rutten粒子滤波的检测前跟踪方法等。
  • 图  1  CRPF滤波器组的结构

    图  2  假设的先验信息与原始先验信息的对比

    图  3  CRPF滤波器组-TBD的检测策略

    图  4  3种测试信号的检测概率及瞬时频率RMSE比较

    图  5  3种测试信号的检测概率及瞬时频率RMSE比较

    图  6  目标出现时刻及持续时间对3种方法的检测概率及瞬时频率RMSE的影响

    图  7  CRPF的数量对CRPF滤波器组-TBD检测性能和估计性能的影响

    图  8  样本数对CRPF滤波器组-TBD检测性能和估计性能的影响

    表  1  本文的CRPF滤波器组-TBD与PF-TBD, Rutten PF-TBD的运行时间(s)比较

    PF-TBD(×10–1)Rutten PF-TBD(×10–1)CRPF滤波器组-TBD(×10–4)
    第1种测试信号1.1352.5351.058
    第2种测试信号3.1255.2182.180
    第3种测试信号3.3525.2442.128
    下载: 导出CSV
  • [1] ZHOU Gongjian, WANG Liangliang, and KIRUBARAJAN T. A pseudo-spectrum approach for weak target detection and tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(6): 3394–3412. doi: 10.1109/TAES.2019.2906419
    [2] TU Xiaotong, HU Yue, LI Fucai, et al. Instantaneous frequency estimation for nonlinear FM signal based on modified polynomial chirplet transform[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(11): 2898–2908. doi: 10.1109/TIM.2017.2730982
    [3] WANG Wei, WANG R, ZHANG Zhimin, et al. First demonstration of airborne SAR with nonlinear FM chirp waveforms[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 247–251. doi: 10.1109/LGRS.2015.2508102
    [4] LIU Jiafang, ZHANG Yunhua, and DONG Xiao. Dechirping compression method for nonlinear frequency modulation waveforms[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(3): 377–381. doi: 10.1109/LGRS.2018.2875893
    [5] SHUI Penglang, BAO Zheng, and SU Hongtao. Nonparametric detection of FM signals using time-frequency ridge energy[J]. IEEE Transactions on Signal Processing, 2008, 56(5): 1749–1760. doi: 10.1109/tsp.2007.909322
    [6] CANDÈS E J, CHARLTON P R, and HELGASON H. Detecting highly oscillatory signals by chirplet path pursuit[J]. Applied and Computational Harmonic Analysis, 2008, 24(1): 14–40. doi: 10.1016/j.acha.2007.04.003
    [7] SALMOND D J and BIRCH H. A particle filter for track-before-detect[C]. 2001 American Control Conference. (Cat. No. 01CH37148), Arlington, USA, 2001: 3755–3760. doi: 10.1109/acc.2001.946220
    [8] LU Jin, SHUI Penglang, and SU Hongtao. Track-before-detect method based on cost-reference particle filter in non-linear dynamic systems with unknown statistics[J]. IET Signal Processing, 2014, 8(1): 85–94. doi: 10.1049/iet-spr.2013.0117
    [9] SHUI Penglang, SHI Sainan, LU Jin, et al. Detection of nonlinear FM signals via forward-backward cost-reference particle filter[J]. Digital Signal Processing, 2016, 48: 104–115. doi: 10.1016/j.dsp.2015.09.016
    [10] RISTIC B, ARULAMPALM S, and GORDON N. Beyond the Kalman Filter: Particle Filters for Tracking Applications[M]. Boston: Artech House, 2004: 239–258.
    [11] RUTTEN M G, RISTIC B, and GORDON N J. A comparison of particle filters for recursive track-before-detect[C]. The 7th International Conference on Information Fusion, Philadelphia, USA, 2005: 169–175. doi: 10.1109/ICIF.2005.1591851.
    [12] YI Wei, FU Lingzhi, GARCÍA-FERNÁNDEZ Á F, et al. Particle filtering based track-before-detect method for passive array sonar systems[J]. Signal Processing, 2019, 165: 303–314. doi: 10.1016/j.sigpro.2019.07.027
    [13] ZHANG Tianzhu, XU Changsheng, and YANG M H. Learning multi-task correlation particle filters for visual tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(2): 365–378. doi: 10.1109/TPAMI.2018.2797062
    [14] VITETTA G M, SIRIGNANO E, DI VIESTI P, et al. Marginalized particle filtering and related filtering techniques as message passing[J]. IEEE Transactions on Signal Processing, 2019, 67(6): 1522–1536. doi: 10.1109/TSP.2019.2893868
    [15] CHEN Hanxin, FAN Dongliang, FANG Lu, et al. Particle swarm optimization algorithm with mutation operator for particle filter noise reduction in mechanical fault diagnosis[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2020, 34(10): 2058012. doi: 10.1142/S0218001420580124
    [16] ZENG Nianyin, WANG Zidong, ZHANG Hong, et al. An improved particle filter with a novel hybrid proposal distribution for quantitative analysis of gold immunochromatographic strips[J]. IEEE Transactions on Nanotechnology, 2019, 18: 819–829. doi: 10.1109/TNANO.2019.2932271
    [17] MÍGUEZ J, BUGALLO M F, and DJURIĆ P M. A new class of particle filters for random dynamic systems with unknown statistics[J]. EURASIP Journal on Advances in Signal Processing, 2004, 2004(15): 303619. doi: 10.1155/S1110865704406039
    [18] MÍGUEZ J. Analysis of parallelizable resampling algorithms for particle filtering[J]. Signal Processing, 2007, 87(12): 3155–3174. doi: 10.1016/j.sigpro.2007.06.011
    [19] NOVEY M, ADALI T, and ROY A. Correspondence: A complex generalized Gaussian distribution-characterization, generation, and estimation[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1427–1433. doi: 10.1109/TSP.2009.2036049
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出版历程
  • 收稿日期:  2021-03-22
  • 修回日期:  2021-08-18
  • 网络出版日期:  2021-09-16
  • 刊出日期:  2021-10-18

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