Full Text:   <6020>

Summary:  <500>

Suppl. Mater.: 

CLC number: TP212.9

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-02-27

Cited: 0

Clicked: 2369

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yunpu ZHANG

https://orcid.org/0000-0002-2300-2207

Qiang FU

https://orcid.org/0000-0003-3414-3272

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.2 P.245-258

http://doi.org/10.1631/FITEE.2200121


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


Author(s):  Yunpu ZHANG, Qiang FU, Ganlin SHAN

Affiliation(s):  Department of Electronic and Optical Engineering, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China

Corresponding email(s):   fq007895@163.com

Key Words:  Sensor scheduling, Area detection, Target tracking, Road constraints, Doppler blind zone


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.

@article{title="A multi-sensor-system cooperative scheduling method for ground area detection and target tracking",
author="Yunpu ZHANG, Qiang FU, Ganlin SHAN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="2",
pages="245-258",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200121"
}

%0 Journal Article
%T A multi-sensor-system cooperative scheduling method for ground area detection and target tracking
%A Yunpu ZHANG
%A Qiang FU
%A Ganlin SHAN
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 2
%P 245-258
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200121

TY - JOUR
T1 - A multi-sensor-system cooperative scheduling method for ground area detection and target tracking
A1 - Yunpu ZHANG
A1 - Qiang FU
A1 - Ganlin SHAN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 2
SP - 245
EP - 258
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200121


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.

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

张昀普,付强,单甘霖
陆军工程大学石家庄校区电子与光学工程系,中国石家庄市,050003
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。

关键词:传感器调度;区域检测;目标跟踪;道路约束;多普勒盲区

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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

[2]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.

[3]Fosbury AM, Crassidis JL, Singh T, et al., 2007. Ground target tracking using terrain information. Proc 10th Int Conf on Information Fusion, p.1-8.

[4]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.

[5]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.

[6]Katsilieris F, Driessen H, Yarovoy A, 2015. Threat-based sensor management for target tracking. IEEE Trans Aerosp Electron Syst, 51(4):2772-2785.

[7]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.

[8]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.

[9]Liang QL, 2008. Automatic target recognition using waveform diversity in radar sensor networks. Patt Recogn Lett, 29(3):377-381.

[10]Luo JH, Tian YX, Chen Y, et al., 2021. Low altitude and small target tracking based on IMM L-M cubature Kalman filter. Proc 24th Int Conf on Information Fusion, p.1-8.

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

[12]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.

[13]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.

[14]Pang C, Shan GL, 2019. Risk-based sensor scheduling for target detection. Comput Electron Eng, 77:179-190.

[15]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.

[16]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.

[17]Shi K, Chen HS, Lin Y, 2015. Probabilistic coverage based sensor scheduling for target tracking sensor networks. Inform Sci, 292:95-110.

[18]Song D, Tharmarasa R, Florea MC, et al., 2019. Multi-vehicle tracking with microscopic traffic flow model-based particle filtering. Automatica, 105:28-35.

[19]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.

[20]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.

[21]Wang Y, Hussein II, Erwin RS, 2011. Risk-based sensor management for integrated detection and estimation. American Control Conf, p.3633-3638.

[22]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.

[23]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.

[24]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.

[25]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.

[26]Wu WH, Sun HM, Huang ZL, et al., 2021a. Multi-GMTI fusion for Doppler blind zone suppression using PHD fusion. Signal Process, 183:108024.

[27]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.

[28]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.

[29]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.

[30]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.

[31]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.

[32]Zheng JH, Gao MG, 2018. Tracking ground targets with a road constraint using a GMPHD filter. Sensors, 18(8):2723.

[33]Zhou GJ, Guo ZK, Li KY, et al., 2021. Motion modeling and state estimation in Range-Doppler plane. Aerosp Sci Technol, 115:106792.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE