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.
BI Xiaojun,HU Songyi
. Firefly Algorithm with High Precision Mixed Strategy
Optimized Particle Filter[J]. Journal of Shanghai Jiaotong University, 2019
, 53(2)
: 232
-238
.
DOI: 10.16183/j.cnki.jsjtu.2019.02.015
［1］NIKNEJAD H T, TAKEUCHI A, MITA S, et al. On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation［J］. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 748-758.
［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.
