基于量子遗传粒子滤波的WSN目标跟踪算法  

Tracking algorithms based on quantum genetic particle filter for WSN

作　　者：杨小莹[1,2] 张万礼[1] 廖同庆[3] 

机构地区：[1]宿州学院,宿州234000 [2]桂林电子科技大学,桂林541004 [3]安徽大学计算智能与信号处理教育部重点实验室,合肥230039

出　　处：《电子测试》2011年第7期1-4,65,共5页Electronic Test

基　　金：安徽省教育厅自然科学研究重点项目(KJ2009A156);安徽省高校优秀青年人才基金重点项目(2010SQRL022ZD)

摘　　要：粒子滤波器是解决非线性非高斯运动跟踪的一种有效方法,很适合于无线传感器网络的目标跟踪。但是粒子滤波算法存在严重的退化现象。常规的重采样方法虽可解决退化问题,但容易导致粒子耗尽。本文针对此问题,将量子遗传算法引入粒子滤波,提出了基于量子遗传粒子滤波的无线传感器网络目标跟踪算法。通过量子遗传算法的编码方式增加粒子集的多样性,从而缓解了粒子滤波的退化现象并解决了粒子耗尽问题,量子的并行性也节省了计算时间,提高了跟踪的实时性。仿真结果表明该算法是可行的。Particle filter is an effective way to solve the problem like tracking objects with Non-Gaussian and Non-linear movement. It is well suited to target tracking in wireless sensor networks, but degeneracy phenomenon is serious. Common re-sampling method can resolve degeneracy phenomenon, but the sample impoverishment is a secondary result. Therefore, tracking algorithms based on quantum genetic particle filter for wireless sensor networks is proposed, in which quantum genetic algorithm is introduced. The diversity of particle sets increase through encoding of the quantum genetic algorithm, thus, the degradation in particle filter is eased and the problem of particle depletion is solved. Quantum parallelism saves the computation time and improved the real-time of tracking. Simulation results show the feasibility of the proposed algorithm.

关 键 词：无线传感器网络 目标跟踪 粒子滤波 量子遗传算法 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]