基于混合引导策略的高精度萤火虫优化粒子滤波算法
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摘要
针对现有群智能优化粒子滤波算法精度较低和收敛速度较慢的问题,提出了一种基于混合引导策略的萤火虫优化粒子滤波算法(MSFA-PF).通过在萤火虫寻优过程中加入混沌扰动搜索策略,以权衡粒子的寻优能力与开发能力;提出一种动态视觉搜索策略,以提高粒子向高似然区域移动的寻优利用率;根据粒子滤波机制设计了新的荧光亮度计算公式,以扩展观测信息,从而提高了粒子质量.仿真结果表明,所提出的MSFA-PF算法能够有效提高智能优化粒子滤波对非线性系统状态估计的精度和速度.
Aiming at the problem of the low precision and slow convergence rate of particle filters based on intelligent optimization algorithms, this paper came up with a firefly algorithm with mixed strategy optimized particle filter. The algorithm is applied to the chaotic perturbation search strategy in the firefly optimization mechanism for balancing the particle optimization ability effectively, and proposed dynamic visual search strategy to improve the utilization ratio of particles moving toward high likelihood regions. At the same time, according to the particle filter mechanism, a new fluorescence luminance formula is designed to expand the observation information for improving the particles quality. Experiment results show that the proposed algorithm effectively improves the accuracy and speed of the intelligent optimization particle filter for nonlinear system state estimation.
Aiming at the problem of the low precision and slow convergence rate of particle filters based on intelligent optimization algorithms, this paper came up with a firefly algorithm with mixed strategy optimized particle filter. The algorithm is applied to the chaotic perturbation search strategy in the firefly optimization mechanism for balancing the particle optimization ability effectively, and proposed dynamic visual search strategy to improve the utilization ratio of particles moving toward high likelihood regions. At the same time, according to the particle filter mechanism, a new fluorescence luminance formula is designed to expand the observation information for improving the particles quality. Experiment results show that the proposed algorithm effectively improves the accuracy and speed of the intelligent optimization particle filter for nonlinear system state estimation.
引文
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[2]李天成,范红旗,孙树栋.粒子滤波理论、方法及其在多目标跟踪中的应用[J].自动化学报, 2015, 41(12):1981-2002. LI Tiancheng, FAN Hongqi, SUN Shudong. Particle filtering:Theory, approach, and application for multitarget tracking[J]. Acta Automatica Sinica, 2015, 41(12):1981-2002.
[3]LI T C, SATTAR T P, HAN Q, et al. Roughening methods to prevent sample impoverishment in the particle PHD filter[C]//Proceedings of the 16th International Conference on Information Fusion. Istanbul, Turkey:IEEE, 2013:77-22.
[4]王伟,余玉揆.多点测试的多模型机动目标跟踪算法[J].自动化学报, 2015, 41(6):1201-1212. WANG Wei, YU Yukui. Multi-try and multi-model particle filter for maneuvering target tracking[J]. Acta Automatica Sinica, 2015, 41(6):1201-1212.
[5]DU M, NAN X M, CUAN L. Monocular human motion tracking by using DE-MC particle filter[J]. IEEE Transactions on Image Processing, 2013, 22(10):3852-3865.
[6]田梦楚,薄煜明,陈志敏,等.萤火虫算法智能优化粒子滤波[J].自动化学报, 2016, 42(1):89-97.TIAN Mengchu, BO Yuming, CHEN Zhimin, et al. Firefly algorithm intelligence optimized particle filter[J]. Acta Automatica Sinica, 2016, 42(1):89-97.
[7]方正,佟国峰,徐心和.粒子群优化粒子滤波方法[J].控制与决策, 2007, 22(3):273-277. FANG Zheng, TONG Guofeng, XU Xinhe. Particle swarm optimized particle filter[J]. Control and Decision, 2007, 22(3):273-277.
[8]ZHANG M, JIA H T, SHEN Z. Improved resampling procedure based on genetic algorithm in particle filter[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(3):344-349.
[9]曹洁,李玉琴,吴迪.基于改进差分进化的高精度粒子滤波算法[J].上海交通大学学报, 2014, 48(12):1714-1720. CAO Jie, LI Yuqin, WU Di. A high precision particle filter based on improved differential evolution[J]. Journal of Shanghai Jiao Tong University, 2014, 48(12):1714-1720.
[10]胡士强,敬忠良.粒子滤波算法综述[J].控制与决策, 2005, 20(4):361-365. HU Shiqiang, JING Zhongliang. Overview of particle filter algorithm[J]. Control and Decision, 2005, 20(4):361-365.
[11]SHAN C F, TAN T N, WEI Y C. Real-time hand tracking using a mean shift embedded particle filter[J]. Pattern Recognition, 2007, 40(7):1958-1970.
[12]朱文超,许德章.一种基于人工萤火虫群优化的改进粒子滤波算法[J].计算机应用研究, 2014, 31(10):2920-2924. ZHU Wenchao, XU Dezhang. Improved particle filter algorithm based on artificial glowworm swamp optimization[J]. Application Research of Computers, 2014, 31(10):2920-2924.
[13]单梁,强浩,李军,等.基于Tent映射的混沌优化算法[J].控制与决策, 2005, 20(2):179-182. SHAN Liang, QIANG Hao, LI Jun, et al. Chaotic optimization algorithm based on Tent map[J]. Control and Decision, 2005, 20(2):179-182.
[14]匡芳君,金忠,徐蔚鸿,等. Tent混沌人工蜂群与粒子群混合算法[J].控制与决策, 2015, 30(5):839-847. KUANG Fangjun, JIN Zhong, XU Weihong, et al. Hybridization algorithm of Tent chaos artificial bee colony and particle swarm optimization[J]. Control and Decision, 2015, 30(5):839-847.