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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 853430, 8 pages
http://dx.doi.org/10.1155/2013/853430
Research Article

Sensor Scheduling with Intelligent Optimization Algorithm Based on Quantum Theory

1Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, School of IoT Engineering, Jiangnan University, Wuxi 214122, China
2Research Centre of Environment Science and Engineering, Wuxi 214063, China

Received 18 July 2013; Revised 3 September 2013; Accepted 4 September 2013

Academic Editor: Ming Li

Copyright © 2013 Zhiguo Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.