未知辐射源信号特征辅助的广义标签多伯努利滤波器

国强; 滕龙; 吴新良; 宋文明; 黄大羽

doi:10.1631/FITEE.2200286

Your Location：

Home >

Browse articles >

未知辐射源信号特征辅助的广义标签多伯努利滤波器

常规文章 | Updated：2022-12-16

- 未知辐射源信号特征辅助的广义标签多伯努利滤波器
- Generalized labeled multi-Bernoulli filter with signal features of unknown emitters
- 信息与电子工程前沿（英文） 2022年23卷第12期页码：1871-1880
- Affiliations：
  
  1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  2.China National Aeronautical Radio Electronics Research Institute, Shanghai 200233, China
- Author bio：
  
  †E-mail: guoqiang@hrbeu.edu.cn;
  ‡Corresponding author
  wuxinliang51@163.com;
  hdayady@163.com
- Funds：
  
  National Major Research and Development Project of China(2018YFE0206500);National Natural Science Foundation of China(62071140);International Scientific and Technological Cooperation Program of China(2015DFR10220);Technology Foundation for Basic Enhancement Plan, China(2021-JCJQ-JJ-0301)
- DOI：10.1631/FITEE.2200286
  中图分类号： TN953
- 收稿：2022-06-30，
  
  录用：2022-10-10，
  
  纸质出版：2022-12-0
- Accepted：
Scan QR Code
国强, 滕龙, 吴新良, 等. 未知辐射源信号特征辅助的广义标签多伯努利滤波器[J]. 信息与电子工程前沿（英文）, 2022,23(12):1871-1880.

Qiang GUO, Long TENG, Xinliang WU, et al. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1871-1880.
国强, 滕龙, 吴新良, 等. 未知辐射源信号特征辅助的广义标签多伯努利滤波器[J]. 信息与电子工程前沿（英文）, 2022,23(12):1871-1880. DOI： 10.1631/FITEE.2200286.

Qiang GUO, Long TENG, Xinliang WU, et al. Generalized labeled multi-Bernoulli filter with signal features of unknown emitters[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1871-1880. DOI： 10.1631/FITEE.2200286.

摘要

提出一种未知辐射源信号特征辅助的广义标签多伯努利滤波器。复杂电磁环境下，辐射源特征通常未知且随时间变化。针对辐射源特征未知的问题，提出一种基于数据场动态聚类的辐射源特征求解方法。针对辐射源特征时变以及对应的概率分布未知的问题，提出一种改进的模糊C-均值算法来计算目标和杂波量测的相关系数，以近似辐射源特征的似然函数。在此基础上，将辐射源特征集成到广义标签多伯努利滤波器中，从而获得新的递归方程。仿真结果表明，提出的方法可以提高对多目标的跟踪性能，尤其在强杂波环境中。

Abstract

A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments

emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem

we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown

an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements

to approximate the EF likelihood function. On this basis

the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets

especially in heavy clutter environments.

关键词

Keywords

references

Bar-Shalom Y , Kirubarajan T , Gokberk C , 2005 . Tracking with classification-aided multiframe data association . IEEE Trans Aerosp Electron Syst , 41 ( 3 ): 868 - 878 . doi: 10.1109/TAES.2005.1541436 http://doi.org/10.1109/TAES.2005.1541436

Battistelli G , Chisci L , Fantacci C , et al. , 2013 . Consensus CPHD filter for distributed multitarget tracking . IEEE J Sel Top Signal Process , 7 ( 3 ): 508 - 520 . doi: 10.1109/JSTSP.2013.2250911 http://doi.org/10.1109/JSTSP.2013.2250911

Cao CH , Zhao YB , 2022 . Range estimation based on symmetry polynomial aided Chinese remainder theorem for multiple targets in a pulse Doppler radar . Front Inform Technol Electron Eng , 23 ( 2 ): 304 - 316 . doi: 10.1631/FITEE.2000418 http://doi.org/10.1631/FITEE.2000418

Chen HM , Li XR , Bar-Shalom Y , 2004 . On joint track initiation and parameter estimation under measurement origin uncertainty . IEEE Trans Aerosp Electron Syst , 40 ( 2 ): 675 - 694 . doi: 10.1109/TAES.2004.1310013 http://doi.org/10.1109/TAES.2004.1310013

Chen HM , Kirubarajan T , Bar-Shalom Y , 2008 . Tracking of spawning targets with multiple finite resolution sensors . IEEE Trans Aerosp Electron Sys t , 44 ( 1 ): 2 - 14 . doi: 10.1109/TAES.2008.4516985 http://doi.org/10.1109/TAES.2008.4516985

Clark D , Ristic B , Vo BN , et al. , 2010 . Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR . IEEE Trans Signal Process , 58 ( 1 ): 26 - 37 . doi: 10.1109/TSP.2009.2030640 http://doi.org/10.1109/TSP.2009.2030640

Da K , Li TC , Zhu YF , et al. , 2020 . Gaussian mixture particle jump-Markov-CPHD fusion for multitarget tracking using sensors with limited views . IEEE Trans Signal Inform Process Netw , 6 : 605 - 616 . doi: 10.1109/TSIPN.2020.3016478 http://doi.org/10.1109/TSIPN.2020.3016478

Da K , Li TC , Zhu YF , et al. , 2021 . Recent advances in multisensor multitarget tracking using random finite set . Front Inform Technol Electron Eng , 22 ( 1 ): 5 - 24 . doi: 10.1631/FITEE.2000266 http://doi.org/10.1631/FITEE.2000266

Guo Q , Nan PL , Wan J , 2016 . Signal classification method based on data mining for multi-mode radar . J Syst Eng Electron , 27 ( 5 ): 1010 - 1017 . doi: 10.21629/JSEE.2016.05.09 http://doi.org/10.21629/JSEE.2016.05.09

Guo YF , Fan KS , Peng DL , et al. , 2015 . A modified variable rate particle filter for maneuvering target tracking . Front Inform Technol Electron Eng , 16 ( 11 ): 985 - 994 . doi: 10.1631/FITEE.1500149 http://doi.org/10.1631/FITEE.1500149

Guo YF , Tharmarasa R , Rajan S , et al. , 2016 . Passive tracking in heavy clutter with sensor location uncertainty . IEEE Trans Aerosp Electron Syst , 52 ( 4 ): 1536 - 1554 . doi: 10.1109/TAES.2016.140820 http://doi.org/10.1109/TAES.2016.140820

Guo YF , Li Y , Ren X , et al. , 2020a . Multiple maneuvering extended target tracking based on Gaussian process . Acta Autom Sin , 46 ( 11 ): 2392 - 2403 (in Chinese) . doi: 10.16383/j.aas.c180849 http://doi.org/10.16383/j.aas.c180849

Guo YF , Li Y , Xue AK , et al. , 2020b . Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes . Signal Process , 172 : 107547 . doi: 10.1016/j.sigpro.2020.107547 http://doi.org/10.1016/j.sigpro.2020.107547

Herrmann M , Hermann C , Buchholz M , 2021 . Distributed implementation of the centralized generalized labeled multi-Bernoulli filter . IEEE Trans Signal Process , 69 : 5159 - 5174 . doi: 10.1109/TSP.2021.3107632 http://doi.org/10.1109/TSP.2021.3107632

Jin B , Li C , Guo J , et al. , 2019 . Multi-target tracking in clutter aided by Doppler information . J Univ Electron Sci Technol China , 48 ( 4 ): 511 - 517 (in Chinese) . doi: 10.3969/j.issn.1001-0548.2019.04.006 http://doi.org/10.3969/j.issn.1001-0548.2019.04.006

Li TC , Hlawatsch F , 2021 . A distributed particle-PHD filter using arithmetic-average fusion of Gaussian mixture parameters . Inform Fus , 73 : 111 - 124 . doi: 10.1016/j.inffus.2021.02.020 http://doi.org/10.1016/j.inffus.2021.02.020

Li TC , Sun SD , Bolić M , et al. , 2016 . Algorithm design for parallel implementation of the SMC-PHD filter . Signal Process , 119 : 115 - 127 . doi: 10.1016/j.sigpro.2015.07.013 http://doi.org/10.1016/j.sigpro.2015.07.013

Li TC , Su JY , Liu W , et al. , 2017 . Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond . Front Inform Technol Electron Eng , 18 ( 12 ): 1913 - 1939 . doi: 10.1631/FITEE.1700379 http://doi.org/10.1631/FITEE.1700379

Li TC , Prieto J , Fan HQ , et al. , 2018 . A robust multi-sensor PHD filter based on multi-sensor measurement clustering . IEEE Commun Lett , 22 ( 10 ): 2064 - 2067 . doi: 10.1109/LCOMM.2018.2863387 http://doi.org/10.1109/LCOMM.2018.2863387

Li TC , Liu Z , Pan Q , 2019 . Distributed Bernoulli filtering for target detection and tracking based on arithmetic average fusion . IEEE Signal Process Lett , 26 ( 12 ): 1812 - 1816 . doi: 10.1109/LSP.2019.2950588 http://doi.org/10.1109/LSP.2019.2950588

Liu C , Sun JP , Lei P , 2018 . δ -generalized labeled multi-Bernoulli filter using amplitude information of neighboring cells . Sensors , 18 ( 4 ): 1153 . doi: 10.3390/s18041153 http://doi.org/10.3390/s18041153

Mahler R , 2007 . PHD filters of higher order in target number . IEEE Trans Aerosp Electron Syst , 43 ( 4 ): 1523 - 1543 . doi: 10.1109/TAES.2007.4441756 http://doi.org/10.1109/TAES.2007.4441756

Mahler RPS , 2003 . Multitarget Bayes filtering via first-order multitarget moments . IEEE Trans Aerosp Electron Syst , 39 ( 4 ): 1152 - 1178 . doi: 10.1109/TAES.2003.1261119 http://doi.org/10.1109/TAES.2003.1261119

Mahler RPS , 2007 . Statistical Multisource-Multitarget Information Fusion . Artech House , Norwood, USA .

Peng H , Huang G , Tian W , et al. , 2018 . Labeled multi-Bernoulli filter based on amplitude information . Syst Eng Electron , 40 ( 12 ): 2636 - 2641 . doi: 10.3969/j.issn.1001-506X.2018.12.03 http://doi.org/10.3969/j.issn.1001-506X.2018.12.03

Ristic B , Vo BT , Vo BN , et al. , 2013 . A tutorial on Bernoulli filters: theory, implementation and applications . IEEE Trans Signal Process , 61 ( 13 ): 3406 - 3430 . doi: 10.1109/TSP.2013.2257765 http://doi.org/10.1109/TSP.2013.2257765

Schuhmacher D , Vo BT , Vo BN , 2008 . A consistent metric for performance evaluation of multi-object filters . IEEE Trans Signal Process , 56 ( 8 ): 3447 - 3457 . doi: 10.1109/TSP.2008.920469 http://doi.org/10.1109/TSP.2008.920469

Sun X , Li RW , Zhou LS , 2020 . Multidimensional information fusion in active sonar via the generalized labeled multi-Bernoulli filter . IEEE Access , 8 : 211335 - 211347 . doi: 10.1109/ACCESS.2020.3039347 http://doi.org/10.1109/ACCESS.2020.3039347

Vo BN , Ma WK , 2006 . The Gaussian mixture probability hypothesis density filter . IEEE Trans Signal Process , 54 ( 11 ): 4091 - 4104 . doi: 10.1109/TSP.2006.881190 http://doi.org/10.1109/TSP.2006.881190

Vo BN , Vo BT , Phung D , 2014 . Labeled random finite sets and the Bayes multi-target tracking filter . IEEE Trans Signal Process , 62 ( 24 ): 6554 - 6567 . doi: 10.1109/TSP.2014.2364014 http://doi.org/10.1109/TSP.2014.2364014

Vo BN , Vo BT , Hoang HG , 2017 . An efficient implementation of the generalized labeled multi-Bernoulli filter . IEEE Trans Signal Process , 65 ( 8 ): 1975 - 1987 . doi: 10.1109/TSP.2016.2641392 http://doi.org/10.1109/TSP.2016.2641392

Wang LP , Zhan RH , Huang Z , et al. , 2021 . Joint tracking and classification of extended targets with complex shapes . Front Inform Technol Electron Eng , 22 ( 6 ): 839 - 861 . doi: 10.1631/FITEE.2000061 http://doi.org/10.1631/FITEE.2000061

Wu WH , Cai YC , Jin HB , et al. , 2021 . Derivation of the multi-model generalized labeled multi-Bernoulli filter: a solution to multi-target hybrid systems . Front Inform Technol Electron Eng , 22 ( 1 ): 79 - 87 . doi: 10.1631/FITEE.2000105 http://doi.org/10.1631/FITEE.2000105

Yi W , Chai L , 2021 . Heterogeneous multi-sensor fusion with random finite set multi-object densities . IEEE Trans Signal Process , 69 : 3399 - 3414 . doi: 10.1109/TSP.2021.3087033 http://doi.org/10.1109/TSP.2021.3087033

Yi W , Jiang M , Hoseinnezhad R , 2017 . The multiple model Vo-Vo filter . IEEE Trans Aerosp Electron Syst , 53 ( 2 ): 1045 - 1054 . doi: 10.1109/TAES.2017.2667300 http://doi.org/10.1109/TAES.2017.2667300

Yi W , Li GC , Battistelli G , 2020 . Distributed multi-sensor fusion of PHD filters with different sensor fields of view . IEEE Trans Signal Process , 68 : 5204 - 5218 . doi: 10.1109/TSP.2020.3021834 http://doi.org/10.1109/TSP.2020.3021834

Zhou YQ , Zhu SL , 2015 . GM-PHD filter with signal features of emitter . Asian J Contr , 17 ( 5 ): 1978 - 1983 . doi: 10.1002/asjc.1040 http://doi.org/10.1002/asjc.1040

Zhu Y , Liang S , Wu XJ , et al. , 2021 . A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain . Front Inform Technol Electron Eng , 22 ( 8 ): 1114 - 1126 . doi: 10.1631/FITEE.2000209 http://doi.org/10.1631/FITEE.2000209

Zhu YQ , 2015 . Research on Tracking Techniques of Multiple Radar Emitter Targets Based on PHD Filter . PhD Thesis, National University of Defense Technology , Changsha, China (in Chinese) .

浏览量

105

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

A multiple maneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order