Decentralized Multi-sensor Scheduling for Multi-target Tracking and Identity Management

2019 ◽  
Author(s):  
Chiyu Zhang ◽  
Inseok Hwang
Keyword(s):  
Target Tracking ◽  
Identity Management ◽  
Sensor Scheduling ◽  
Multi Target Tracking

scholarly journals Adaptive Sensor Scheduling and Resource Allocation in Netted Collocated MIMO Radar System for Multi-Target Tracking

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109976-109988
Author(s):  
Zhengjie Li ◽  
Junwei Xie ◽  
Haowei Zhang ◽  
Houhong Xiang ◽  
Zhaojian Zhang
Keyword(s):  
Resource Allocation ◽  
Target Tracking ◽  
Mimo Radar ◽  
Radar System ◽  
Sensor Scheduling ◽  
Multi Target Tracking

scholarly journals Multi-Sensor Optimization Scheduling for Target Tracking Based on PCRLB and a Novel Intercept Probability Factor

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 140 ◽  
Author(s):  
Gongguo Xu ◽  
Ce Pang ◽  
Xiusheng Duan ◽  
Ganlin Shan
Keyword(s):  
Target Tracking ◽  
Pso Algorithm ◽  
Sensor Scheduling ◽  
Tracking Accuracy ◽  
Active Sensors ◽  
Intercept Probability ◽  
Multi Target Tracking ◽  
Fast Solution ◽  
Tracking Model ◽  
Set Up

In order to improve the survivability of active sensors, the problem of low probability of intercept (LPI) for a multi-sensor network system is studied in this paper. Two kinds of operational requirements are taken into account, the first of which is to ensure the survivability of sensors and the second is to improve the tracking accuracy of targets as much as possible. Firstly, the sensor tracking model and the posterior Carmér-Rao lower bound (PCRLB) of the target are presented to evaluate the sensor tracking benefits in next time. Then, a novel intercept probability factor (IPF) is proposed for multi-sensor multi-target tracking scenarios. At the basis of PCRLB and IPF, a myopic multi-sensor scheduling model for target tracking is set up to control the intercepted probability of sensors and improve the target tracking accuracy. At last, a fast solution algorithm based on an improved particle swarm optimization (PSO) algorithm is given to obtain the optimal scheduling actions. Simulation of experimental results show that the proposed model can effectively control the intercepted risk of every sensor, which can also obtain better target tracking performance than existing multi-sensor scheduling methods.

Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter

2019 ◽  
Vol 2019 (13) ◽  
pp. 127-1-127-7
Author(s):  
Benjamin J. Foster ◽  
Dong Hye Ye ◽  
Charles A. Bouman
Keyword(s):  
Target Tracking ◽  
Vision Sensor ◽  
Multi Target Tracking ◽  
Event Based

Shared-memory parallel multi-target tracking

2009 ◽  
Vol 28 (9) ◽  
pp. 2303-2305
Author(s):  
Xiao-gang WANG ◽  
Xiao-juan WU ◽  
Xin ZHOU ◽  
Xiao-yan ZHANG
Keyword(s):  
Target Tracking ◽  
Shared Memory ◽  
Multi Target Tracking
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