Improved SMC cardinality-balanced multi-Bernoulli forwardbackward smoothing track-before-detect algorithm

Jiazheng PEI; Yong HUANG; Yunlong DONG; Xiaolong CHEN

doi:10.11959/j.issn.1000-436x.2019098

您当前的位置：

首页 >

文章列表页 >

Improved SMC cardinality-balanced multi-Bernoulli forwardbackward smoothing track-before-detect algorithm

Papers | 更新时间：2024-06-05

- Improved SMC cardinality-balanced multi-Bernoulli forwardbackward smoothing track-before-detect algorithm
- Journal on Communications Vol. 40, Issue 8, Pages: 102-113(2019)
- 作者机构：
  
  海军航空大学，山东烟台 264001
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U1633122);The National Natural Science Foundation of China(61871391);The National Natural Science Foundation of China(61501487);The National Natural Science Foundation of China(61471382);The National Natural Science Foundation of China(61531020);China Postdoctoral Science Foundation Project(2017M620862);The Key Research and Development Program of Shandong Province(2019GSF111004);The Special Funds of Young Talents Program of CAST(YESS20160115);The National Key Research and Development Program of China(2016YFC0800406)
- DOI：10.11959/j.issn.1000-436x.2019098
  CLC： TN953
- Online First：2019-08，
  
  Published：25 August 2019
- 稿件说明：
移动端阅览
Jiazheng PEI, Yong HUANG, Yunlong DONG, et al. Improved SMC cardinality-balanced multi-Bernoulli forwardbackward smoothing track-before-detect algorithm[J]. Journal on Communications, 2019, 40(8): 102-113.
DOI：

Jiazheng PEI, Yong HUANG, Yunlong DONG, et al. Improved SMC cardinality-balanced multi-Bernoulli forwardbackward smoothing track-before-detect algorithm[J]. Journal on Communications, 2019, 40(8): 102-113. DOI： 10.11959/j.issn.1000-436x.2019098.

摘要

针对在雷达观测下机动弱小目标的检测前跟踪（TBD）问题中，基于序贯蒙特卡洛的势均衡多伯努利检测前跟踪（SMC-CBMeMBer-TBD）算法存在目标的数目估计不准确及状态估计精度随时间下降的问题，提出了一种基于SMC-CBMeMBer前向后向平滑检测前跟踪的改进算法。该算法在预测和更新过程之间加入多目标粒子群优化算法（MOPSO），基于观测值设置适应度目标函数，使粒子集群向后验概率密度较为集中的位置分布，缓解了粒子贫乏的问题；在更新步骤之后加入平滑递归方法，利用观测值平滑滤波值，算法运算时间虽有一定延长，但获得了数目和状态估计精度的提升。仿真实验表明，与CBMeMBer-TBD方法相比，所提算法在对机动目标数目估计和目标状态估计的准确度等性能上都有所改进。

Abstract

For the tracking problem of multiple maneuvering targets in radar observation

the sequential Monte-Carlo cardinality-balanced multi-Bernoulli track-before-detect (SMC-CBMeMBer-TBD) algorithm is inaccurate in the estimation of the number of targets and the precision of state estimation.An improved algorithm based on SMC-CBMeMBer forward backward smoothing track-before-detect algorithm was proposed.In the algorithm

the multi target particle swarm optimization (MOPSO) was added between the process of prediction and update

and the fitness function was set up based on the observation value to make the particle set move to the position of the larger posterior probability density distribution

and solve the particle poverty in the heavy sampling process.In the update step

the algorithm was used.Then the smoothing recursive method was added

and the arithmetic operation time was prolonged

but the number and the state estimation precision were improved.The simulation results show that compared with the CBMeMBer-TBD method

the proposed algorithm improves the accuracy of the estimation of the number of maneuvering targets and the accuracy of the target state estimation.

关键词

Keywords

references

蒋鹏 , 宋华华 , 林广 . 基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法 [J ] . 通信学报 , 2013 , 34 ( 11 ): 8 - 17 .

JIANG P , SONG H H , LIN G . Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropo-lis-hasting sampling particle filter [J ] . Journal on Communications , 2013 , 34 ( 11 ): 8 - 17 .

戴江安 , 邱天爽 . 基于检测前跟踪的声源跟踪算法 [J ] . 通信学报 , 2017 , 38 ( 2 ): 67 - 73 .

DAI J A , QIU T S . Acoustic source tracking algorithm using track before detect [J ] . Journal on Communications , 2017 , 38 ( 2 ): 67 - 73 .

MAHLER R . Statistical multisource-multitarget information fusion [M ] . Boston : Artech HousePress , 2007 .

MAHLER R . Multitarget Bayes filtering via first-order multitarget moments [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2003 , 39 ( 4 ): 1152 - 1178 .

MAHLER R . PHD filters of higher order in target number [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2007 , 43 ( 4 ): 1523 - 1543 .

王慧斌 , 陈哲 , 王鑫 , 等 . 基于随机有限集的UPF-CPHD多目标跟踪 [J ] . 通信学报 , 2012 , 33 ( 12 ): 147 - 153 .

WANG H B , CHEN Z , WANG X , et al . Random finite sets based UPF-CPHD multi-object tracking [J ] . Journal on Communications , 2012 , 33 ( 12 ): 147 - 153 .

VO B T , VO B N , CAMTONI A . Bayesian filtering with random finite set observations [J ] . IEEE Transactions on Signal Processing , 2018 , 56 ( 4 ): 1313 - 1326 .

VO B T , VO B N , CANTONI A . The cardinality balanced multi-target multi-Bernoulli filter and its implement [J ] . IEEE Transactions on Signal Processing , 2009 , 57 ( 2 ): 409 - 423 .

朱红鹏 , 黄勇 , 修建娟 , 等 . 基于GM-PHD平滑器的检测前跟踪技术 [J ] . 雷达科学与技术 , 2016 , 14 ( 6 ): 648 - 653 .

ZHU H P , HUANG Y , XIU J J , et al . Track-before-detect algorithm using GM-PHD smoothing filter [J ] . Radar Science and Technology , 2016 , 14 ( 6 ): 648 - 653 .

VO B T , CLARK D , VO B N , et al . Bernoulli forward-backward smoothing for joint target detection and tracking [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 9 ): 4473 - 4477 .

WONG S , VO B T , PAPI F . Bernoulli forward-backward smoothing for track-before-detect [J ] . IEEE Signal Processing Letters , 2014 , 21 ( 6 ): 727 - 731 .

VO B T , SEE C M , MA N , et al . Multi-sensor joint detection and tracking with the Bernoulli filter [J ] . IEEE Transactions on Aerospace and Electronic Systems , 2012 , 48 ( 2 ): 1385 - 1402 .

孙杰 , 李冬 . 多目标的多伯努利平滑方法 [J ] . 数字通信 , 2014 , 41 ( 2 ): 8 - 11 .

SUN J , LI D . Multi-Bernoulli smoother for multi-target tracking [J ] . Digital Communication , 2014 , 41 ( 2 ): 8 - 11 .

WONG J , VO B T , VO B N , et al . Multi-Bernoulli based track-before-detect with road constraints [C ] // International Conference on Information Fusion . IEEE , 2012 : 840 - 846 .

柳超 , 关键 , 黄勇 , 等 . 基于PHD的多目标检测前跟踪改进方法 [J ] . 雷达科学与技术 , 2016 , 14 ( 1 ): 1 - 6 .

LIU C , GUAN J , HUANG Y , et al . An improved multitarget track-before-detect algorithm based on probability hypothesis density filter [J ] . Radar Science and Technology , 2016 , 14 ( 1 ): 1 - 6 .

占荣辉 , 刘盛启 , 欧建平 , 等 . 基于序贯蒙特卡罗概率假设密度滤波的多目标检测前跟踪改进算法 [J ] . 电子与信息学报 , 2014 , 36 ( 11 ): 2593 - 2598 .

ZHAN R H , LIU S Q , OU J P , et al . Improved multitarget track before detect algorithm using the sequential Monte Carlo probability hypoth-esis density filter [J ] . Journal of Electronics ＆ Information Technology , 2014 , 36 ( 11 ): 2593 - 2598 .

林再平 , 周一宇 , 安玮 , 等 . 基于概率假设密度滤波平滑器的检测前跟踪算法 [J ] . 光学学报 , 2012 , 32 ( 10 ): 124 - 131 .

LIN Z P , ZHOU Y Y , AN W , et al . Track-before-detect algorithm based on probability hypothesis density smoother [J ] . Acta Optica Sinica , 2012 , 32 ( 10 ): 124 - 131 .

李宁 . 基于 MeMBer 滤波器的弱小目标检测前跟踪方法研究 [D ] . 西安:西安电子科技大学 , 2015 .

LI N . Research on tracking before detection algorithms of dim-small targets based on MeMBer filter [D ] . Xi’an:Xidian University , 2015 .

曹潇男 . 基于随机有限集理论的检测前跟踪方法研究 [D ] . 西安:西安电子科技大学 , 2014 .

CAO X N . Study of tracking before detection based on random finite set theory [D ] . Xi’an:Xidian University , 2014 .

YANG C Q , SHI Z G , HAN K , et al . Optimization of particle CBMeMBer filters for hardware implement [J ] . IEEE Transections on Vehicular Technology , 2018 ,PP( 99 ):1.

LIU J S , CHEN R , LOGVINENKO T . A theoretical framework for sequential importance sampling with resampling [M ] // Sequential Monte Carlo Methods in Practice . New York:Springer , 2001 .

方正 , 佟国锋 , 徐心和 . 粒子群优化粒子滤波方法 [J ] . 控制与决策 , 2007 , 22 ( 3 ): 273 - 277 .

FANG Z , TONG G F , XU X H . Particle swarm optimized particle filter [J ] . Control and Decision , 2007 , 22 ( 3 ): 273 - 277 .

KENNEDY J , EBERHART R . Particle swarm optimization [C ] // IEEE International Conference on Neural Networks . IEEE , 1995 : 1941 - 1948 .

汲清波 , 耿丽群 , 任超 . 高斯粒子群优化粒子滤波的检测前跟踪算法 [J ] . 计算机工程与应用 , 2014 , 50 ( 17 ): 205 - 209 .

JI Q B , GENG L Q , REN C . Track before detect algorithm based on Gaussian particle swarm optimiza-tion particle filter [J ] . Computer En-gineering and Applications , 2014 , 50 ( 17 ): 205 - 209 .

余晓东 , 雷英杰 , 岳韶华 , 等 . 基于粒子群优化的直觉模糊核聚类算法研究 [J ] . 通信学报 , 2015 , 36 ( 5 ): 74 - 80 .

YU X D , LEI Y J , YUE S H , et al . Research on PSO-based intuition-istic fuzzy kernel clustering algorithm [J ] . Journal on Communications , 2015 , 36 ( 5 ): 74 - 80 .

康岚兰 , 董文永 , 宋婉娟 , 等 . 无惯性自适应精英变异反向粒子群忧化算法 [J ] . 通信学报 , 2017 , 38 ( 8 ): 66 - 78 .

KANG L L , DONG W Y , SONG W J , et al . Non-inertial opposi-tion-based particle swarm optimization with adaptive elite mutation [J ] . Journal on Communications , 2017 , 38 ( 8 ): 66 - 78 .

DEB K , PRATAP A , AGARWAL S , et al . A fast and elitist multi-objective genetic algorithm:NSGA-II [J ] . IEEE Transactions on Evolutionary Computation , 2002 , 6 ( 2 ): 182 - 197 .

LI H , ZHANG Q . Multiobjective optimization problems with complicated Pareto sets,MOEA/D and NSGA-II [J ] . IEEE Transactions on Evolutionary Computation , 2009 , 13 ( 2 ): 284 - 302 .

VO B N , VO B T , PHAM N T , et al . Joint detection and estimation of multiple objects from image observations [J ] . IEEE Transactions on Signal Processing , 2010 , 58 ( 10 ): 5129 - 5241 .

RISTIC B , VO B N , CLARK D , et al . A metric for performance evaluation of multi-target tracking algorithms [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 7 ): 3452 - 3457 .

Views

1181

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter

DGA malicious domain name identification based on XGBoost and particle swarm optimization algorithm

Routing algorithm for railway monitoring linear WSN based on improved PSO

Research on computing offloading method for maritime observation monitoring sensor network

Approach of target tracking combining particle filter and metric learning

Related Author

Guang LIN

Hua-hua SONG

Peng JIANG

CHEN Weijie

FENG Lichun

ZHOU Min

CHEN Zesheng

Xuejie WANG

Related Institution

Zhejiang Province EnvironmentalMonitoring

Institute of Information and Control, Hangzhou Dianzi University

Information Technology Center, Guangzhou Academy of Fine Arts

School of Electronic and Information Engineering, Lanzhou Jiaotong University

Institute of Computing Technology, Chinese Academy of Sciences

AI问答

⁰