Sensor Scheduling For Target Tracking Using Particle Swarm Optimization

Author(s):  
S. Maheswararajah ◽  
S. Halgamuge
2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Zhiguo Chen ◽  
Yi Fu ◽  
Wenbo Xu

The particle swarm optimization (PSO) algorithm superiority exists in convergence rate, but it tends to get stuck in local optima. An improved PSO algorithm is proposed using a best dimension mutation technique based on quantum theory, and it was applied to sensor scheduling problem for target tracking. The dynamics of the target are assumed as linear Gaussian model, and the sensor measurements show a linear correlation with the state of the target. This paper discusses the single target tracking problem with multiple sensors using the proposed best dimension mutation particle swarm optimization (BDMPSO) algorithm for various cases. Our experimental results verify that the proposed algorithm is able to track the target more reliably and accurately than previous ones.


2019 ◽  
Vol 27 (5) ◽  
pp. 1206-1217
Author(s):  
郭巳秋 GUO Si-qiu ◽  
张 涛 ZHANG Tao ◽  
苗锡奎 MIAO Xi-kui

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