基于量子遗传粒子滤波的无线传感器网络的目标跟踪算法的研究

文章主要研究的是关于量子遗传粒子滤波跟踪算法,该算法适用于无线传感器网络目标跟踪;用传统的粒子重采样可以解决粒子退化问题,但容易导致粒子逐渐减少,甚至耗尽;针对粒子耗尽问题,采用量子遗传算法,其独特的量子遗传的编码方式有效保证粒子组成的多样性,从而减缓了粒子滤波的退化现象,解决了粒子耗尽问题;通过引入量子的概念,同时让其并行可以有效地减少算法的运行时间,实时跟踪性能得到了大大提高;通过仿真结果表明该算法具有有效性、可行性;通过仿真:PF,GAPF、QGPF算法运行时间分别是56.16 s,46.71 s和30.46 s;全面比较后,QGPF算法计算时间最短;跟踪精度用均方根误差表示,与GAPF算法和PF算法相比QGPF算法位置和算法速度的中均方根误差最低(位置为0.0302,0.0258,速度0.0201,0.0101),其中PF算法的跟踪精度最低,QGPF跟踪精度最高,进一步表明QGPF算法具有良好的跟踪性能;实验结果表明,在大噪声条件下量子遗传粒子滤波算法改善了粒子贫乏问题,缩短跟踪时间和提高跟踪位置的精确性,鲁棒性,算法具有显著的优点。

Research on Target Tracking Algorithm of Wireless Sensor Networks Based on Quantum Genetic Particle Filter

Abstract : A quantum genetic particle filter tracking algorithm is proposed in this paper. Quantum genetic algorithm (QGA) is used to increase the diversity of the particle set, so as to alleviate the degradation of particle filter and solve the problem of particle depletion. Quantum parallelism also saves computing time and improves the real-time performance of tracking. The simulation results show that the algorithm is feasible. The simulation results show that compared with the original algorithm, the quantum genetic particle filter algorithm can improve the particle impoverishment problem and the tracking accuracy under the condition of large noise.