A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

Yunpu ZHANG; Qiang FU; Ganlin SHAN

doi:10.1631/FITEE.2200121

Your Location：

Home >

Browse articles >

A multi-sensor-system cooperative scheduling method for ground area detection and target tracking

Regular Papers | Updated：2023-02-27

- A multi-sensor-system cooperative scheduling method for ground area detection and target tracking
- 一种面向地面区域检测和目标跟踪的多传感器系统协同调度方法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 2, Pages: 245-258(2023)
- Affiliations：
  
  Department of Electronic and Optical Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200121
  CLC： TP212.9
- Received：27 March 2022，
  
  Revised：2022-08-16，
  
  Published：0 February 2023
- Accepted：
Scan QR Code
Yunpu ZHANG, Qiang FU, Ganlin SHAN. A multi-sensor-system cooperative scheduling method for ground area detection and target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(2): 245-258.
DOI：

Yunpu ZHANG, Qiang FU, Ganlin SHAN. A multi-sensor-system cooperative scheduling method for ground area detection and target tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(2): 245-258. DOI： 10.1631/FITEE.2200121.

摘要

本文提出一种面向多任务协同的多传感器系统协同调度方法，并将其应用于地面区域检测和目标跟踪。调度的目的是选择最佳的传感器来完成分配的作战任务，并获得最佳作战收益。首先建立区域检测模型，并提出检测风险的计算方法以量化在调度中的检测收益。然后结合道路约束信息和多普勒盲区信息建立地面目标跟踪模型，并引入后验克拉美罗下限评估未来时刻的跟踪精度。最后，考虑检测、跟踪和能耗控制的需求建立目标函数，通过求解目标函数，得到最优的传感器调度方案。仿真结果表明，所提传感器调度方法可以选择合适的传感器完成所需作战任务，并在区域检测、目标跟踪和能耗控制方面均具有良好性能。

Abstract

A multi-sensor-system cooperative scheduling method for multi-task collaboration is proposed in this paper. We studied the method for application in ground area detection and target tracking. The aim of sensor scheduling is to select the optimal sensors to complete the assigned combat tasks and obtain the best combat benefits. First

an area detection model was built

and the method of calculating the detection risk was proposed to quantify the detection benefits in scheduling. Then

combining the information on road constraints and the Doppler blind zone

a ground target tracking model was established

in which the posterior Carmér-Rao lower bound was applied to evaluate future tracking accuracy. Finally

an objective function was developed which considers the requirements of detection

tracking

and energy consumption control. By solving the objective function

the optimal sensor-scheduling scheme can be obtained. Simulation results showed that the proposed sensor-scheduling method can select suitable sensors to complete the required combat tasks

and provide good performance in terms of area detection

target tracking

and energy consumption control.

关键词

Keywords

references

Beard M , Vo BT , Vo BN , et al. , 2015 . Sensor control for multi-target tracking using Cauchy-Schwarz divergence . Proc 18 th Int Conf on Information Fusion , p. 937 - 944 .

Chhetri AS , Morrell D , Papandreou-Suppappola A , 2006 . Nonmyopic sensor scheduling and its efficient implementation for target tracking applications . EURASIP J Adv Signal Process , 2006 ( 1 ): 031520 . https://doi.org/10.1155/ASP/2006/31520 https://doi.org/10.1155/ASP/2006/31520

Fosbury AM , Crassidis JL , Singh T , et al. , 2007 . Ground target tracking using terrain information . Proc 10 th Int Conf on Information Fusion , p. 1 - 8 . https://doi.org/10.1109/ICIF.2007.4408079 https://doi.org/10.1109/ICIF.2007.4408079

Habibi J , Mahboubi H , Aghdam AG , 2016 . Distributed coverage control of mobile sensor networks subject to measurement error . IEEE Trans Autom Contr , 61 ( 11 ): 3330 - 3343 . https://doi.org/10.1109/TAC.2016.2521370 https://doi.org/10.1109/TAC.2016.2521370

Hernandez M , Benavoli A , Graziano A , et al. , 2011 . Performance measures and MHT for tracking move-stop-move targets with MTI sensors . IEEE Trans Aerosp Electron Syst , 47 ( 2 ): 996 - 1025 . https://doi.org/10.1109/TAES.2011.5751239 https://doi.org/10.1109/TAES.2011.5751239

Katsilieris F , Driessen H , Yarovoy A , 2015 . Threat-based sensor management for target tracking . IEEE Trans Aerosp Electron Syst , 51 ( 4 ): 2772 - 2785 . https://doi.org/10.1109/TAES.2015.140052 https://doi.org/10.1109/TAES.2015.140052

Li D , Sun PC , Chen W , 2016 . A multi-sensor management method based on particle swarm algorithm . IEEE Information Technology , Networking, Electronic and Automation Control Conf , p. 766 - 770 . https://doi.org/10.1109/ITNEC.2016.7560465 https://doi.org/10.1109/ITNEC.2016.7560465

Li Y , Krakow LW , Chong EKP , et al. , 2009 . Approximate stochastic dynamic programming for sensor scheduling to track multiple targets . Dig Signal Process , 19 ( 6 ): 978 - 989 . https://doi.org/10.1016/j.dsp.2007.05.004 https://doi.org/10.1016/j.dsp.2007.05.004

Liang QL , 2008 . Automatic target recognition using waveform diversity in radar sensor networks . Patt Recogn Lett , 29 ( 3 ): 377 - 381 . https://doi.org/10.1016/j.patrec.2007.10.016 https://doi.org/10.1016/j.patrec.2007.10.016

Luo JH , Tian YX , Chen Y , et al. , 2021 . Low altitude and small target tracking based on IMM L-M cubature Kalman filter . Proc 24 th Int Conf on Information Fusion , p. 1 - 8 . https://doi.org/10.23919/FUSION49465.2021.9626903 https://doi.org/10.23919/FUSION49465.2021.9626903

Mertens M , Nickel U , 2011 . GMTI tracking in the presence of Doppler and range ambiguities . Proc 14 th Int Conf on Information Fusion , p. 1369 - 1376 .

Misra S , Singh A , Chatterjee S , et al. , 2015 . QoS-aware sensor allocation for target tracking in sensor-cloud . Ad Hoc Netw , 33 : 140 - 153 . https://doi.org/10.1016/j.adhoc.2015.04.009 https://doi.org/10.1016/j.adhoc.2015.04.009

Oh H , Kim S , Tsourdos A , 2015 . Road-map–assisted standoff tracking of moving ground vehicle using nonlinear model predictive control . IEEE Trans Aerosp Electron Syst , 51 ( 2 ): 975 - 986 . https://doi.org/10.1109/TAES.2014.130688 https://doi.org/10.1109/TAES.2014.130688

Pang C , Shan GL , 2019 . Risk-based sensor scheduling for target detection . Comput Electron Eng , 77 : 179 - 190 . https://doi.org/10.1016/j.compeleceng.2019.05.014 https://doi.org/10.1016/j.compeleceng.2019.05.014

Shan GL , Zhang ZN , 2017 . Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP . Trans Inst Meas Contr , 39 ( 2 ): 230 - 243 . https://doi.org/10.1177/0142331215604211 https://doi.org/10.1177/0142331215604211

Shan GL , Xu GG , Qiao CL , 2020 . A non-myopic scheduling method of radar sensors for maneuvering target tracking and radiation control . Def Technol , 16 ( 1 ): 242 - 250 . https://doi.org/10.1016/j.dt.2019.10.001 https://doi.org/10.1016/j.dt.2019.10.001

Shi K , Chen HS , Lin Y , 2015 . Probabilistic coverage based sensor scheduling for target tracking sensor networks . Inform Sci , 292 : 95 - 110 . https://doi.org/10.1016/j.ins.2014.08.067 https://doi.org/10.1016/j.ins.2014.08.067

Song D , Tharmarasa R , Florea MC , et al. , 2019 . Multi-vehicle tracking with microscopic traffic flow model-based particle filtering . Automatica , 105 : 28 - 35 . https://doi.org/10.1016/j.automatica.2019.03.016 https://doi.org/10.1016/j.automatica.2019.03.016

Ulmke M , Erdinc O , Willett P , 2010 . GMTI tracking via the Gaussian mixture cardinalized probability hypothesis density filter . IEEE Trans Aerosp Electron Syst , 46 ( 4 ):‍ 1821 - 1833 . https://doi.org/10.1109/TAES.2010.5595597 https://doi.org/10.1109/TAES.2010.5595597

Wan KF , Gao XG , Li B , et al. , 2018 . Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets . J Syst Eng Electron , 29 ( 1 ): 74 - 85 . https://doi.org/10.21629/JSEE.2018.01.08 https://doi.org/10.21629/JSEE.2018.01.08

Wang Y , Hussein II , Erwin RS , 2011 . Risk-based sensor management for integrated detection and estimation . American Control Conf , p. 3633 - 3638 . https://doi.org/10.1109/ACC.2011.5991348 https://doi.org/10.1109/ACC.2011.5991348

Wang Y , Wang XG , Shan YZ , et al. , 2020 . Quantized genetic resampling particle filtering for vision-based ground moving target tracking . Aerosp Sci Technol , 103 : 105925 . https://doi.org/10.1016/j.ast.2020.105925 https://doi.org/10.1016/j.ast.2020.105925

Wu WH , Liu WJ , Jiang J , et al. , 2016 . GM-PHD filter-based multi-target tracking in the presence of Doppler blind zone . Dig Signal Process , 52 : 1 - 12 . https://doi.org/10.1016/j.dsp.2016.01.014 https://doi.org/10.1016/j.dsp.2016.01.014

Wu WH , Sun HM , Cai YC , et al. , 2020a . MM-GLMB filter-based sensor control for tracking multiple maneuvering targets hidden in the Doppler blind zone . IEEE Trans Signal Process , 68 : 4555 - 4567 . https://doi.org/10.1109/TSP.2020.3009497 https://doi.org/10.1109/TSP.2020.3009497

Wu WH , Sun HM , Cai YC , et al. , 2020b . Tracking multiple maneuvering targets hidden in the DBZ based on the MM-GLMB filter . IEEE Trans Signal Process , 68 : 2912 - 2924 . https://doi.org/10.1109/TSP.2020.2988635 https://doi.org/10.1109/TSP.2020.2988635

Wu WH , Sun HM , Huang ZL , et al. , 2021a . Multi-GMTI fusion for Doppler blind zone suppression using PHD fusion . Signal Process , 183 : 108024 . https://doi.org/10.1016/j.sigpro.2021.108024 https://doi.org/10.1016/j.sigpro.2021.108024

Wu WH , Sun HM , Huang WP , et al. , 2021b . Multi-GMTI decentralized tracking via consensus LMB density fusion . Int Conf on Control, Automation and Information Sciences , p. 122 - 129 . https://doi.org/10.1109/ICCAIS52680.2021.9624574 https://doi.org/10.1109/ICCAIS52680.2021.9624574

Xu GG , Shan GL , Duan XS , 2019 . Non-myopic scheduling method of mobile sensors for manoeuvring target tracking . IET Radar Sonar Navig , 13 ( 11 ): 1899 - 1908 . https://doi.org/10.1049/iet-rsn.2019.0178 https://doi.org/10.1049/iet-rsn.2019.0178

Yang C , Blasch E , Patrick J , et al. , 2010 . Ground target track bias estimation using opportunistic road information . IEEE National Aerospace & Electronics Conf , p. 156 - 163 . https://doi.org/10.1109/NAECON.2010.5712940 https://doi.org/10.1109/NAECON.2010.5712940

Yu M , Oh H , Chen WH , 2016 . An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information . Aerosp Sci Technol , 52 : 62 - 69 . https://doi.org/10.1016/j.ast.2016.02.016 https://doi.org/10.1016/j.ast.2016.02.016

Zhang S , Bar-Shalom Y , 2011 . Track segment association for GMTI tracks of evasive move-stop-move maneuvering targets . IEEE Trans Aerosp Electron Syst , 47 ( 3 ): 1899 - 1914 . https://doi.org/10.1109/taes.2011.5937272 https://doi.org/10.1109/taes.2011.5937272

Zheng JH , Gao MG , 2018 . Tracking ground targets with a road constraint using a GMPHD filter . Sensors , 18 ( 8 ): 2723 . https://doi.org/10.3390/s18082723 https://doi.org/10.3390/s18082723

Zhou GJ , Guo ZK , Li KY , et al. , 2021 . Motion modeling and state estimation in Range-Doppler plane . Aerosp Sci Technol , 115 : 106792 . https://doi.org/10.1016/j.ast.2021.106792 https://doi.org/10.1016/j.ast.2021.106792

Views

249

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Joint target tracking using an autonomous underwater vehicle and underwater sensor networks for underwater applications

Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking

High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain

Target tracking methods based on a signal-to-noise ratio model

Related Author

Yi YANG

Boon-Chong SEET

Zhenkai ZHANG

Zhaohong LV

Wenming SONG

Xinliang WU

Yunfei GUO

Tianxiang YIN

Related Institution

Wuhan Maritime Communication Research Institute

Electrical and Electronic Engineering Department, Auckland University of Technology

Ocean College, Jiangsu University of Science and Technology

College of Automation, Hangzhou Dianzi University

China National Aeronautical Radio Electronics Research Institute

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰