基于粒子群优化的粒子滤波算法精度不高，运算复杂度大，难以在实际工程中应用. 为此，文中提出一种新型邻域自适应调整的动态粒子群优化粒子滤波算法. 该算法考虑了粒子的邻域信息，利用多样性因子、邻域扩展因子和邻域限制因子共同对粒子的邻域粒子数量进行自适应调整，控制粒子对邻域的影响，减轻局部最优现象，达到收敛速度和寻优能力的最佳平衡. 利用UNGM模型、目标跟踪模型以及故障检测模型对算法的性能进行仿真测试，结果表明：该算法与PSO-PF相比提高了精度和运算速度，具有实际工程应用价值.
Particle filter based on particle swarm optimization (PSO-PF) algorithm suffers from low precision and high computation complexity, therefore is difficult to be used in practical applications. This paper proposes novel dynamic particle filter algorithm based on neighborhood adaptive particle swarm optimization (DPSOPF). The method takes the neighborhood information of particles into consideration. Factors of diversity, neighborhood extension, and neighborhood limiting are jointly used to realize self-adaption of neighborhood particle numbers. Thus the influence of particles on the neighborhood is under control, and the local optimization phenomenon is alleviated. Optimal balance is achieved between convergence speed and search ability. By using the univariate nonstationary growth model (UNGM), target tracking model and failure detection model, a simulation test of the algorithm is performed. The results show that, compared to PSO-PF, the proposed algorithm improves precision and computation speed, showing its applicability to practical engineering.
[1] 栾海妍，江桦，刘小宝. 利用粒子滤波与支持向量机的数字混合信号单通道盲分离[J]. 应用科学学报，2011, 29 (2): 195-202
LUAN Haiyan, JIANG Hua, LIU Xiaobao. Single channel blind source separation of digital mixtures using particle filtering and support vector machine[J]. Journal of Applied Sciences, 2011, 29 (2): 195-202. (in Chinese)
[2] GORDON N, SALMOND D J, SMITH A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation [C]//IEEE Proceedings F: Radar and Signal Processing, 1993, 140(2):107-113
[3] DOUCET A, GODSILL S. On sequential Monte Carlo sampling methods for Bayesian filtering [J]. Statistics and Compuring, 2000, 10(1): 197-208.
[4] KONG A, LIU J. Sequential imputations and Bayesian missing data problems [J]. Journal American Statistical Association, 1994, 89(2): 278-288.
[5] UASAKI K, HATANAKA T. Evolution strategies based particle filter for fault detection [C]// proceedings of the IEEE symposium on computational intelligence in image and signal processing, Hawaiian, USA: IEEE, 2007: 58-65
[6] 杨璐，李明，张鹏. 一种新的改进粒子滤波算法 [J]. 西安电子科技大学学报，2010, 37(5): 862-865.
YANG Lu, LI Ming, ZHANG Peng. New improved particle filter algorithm [J]. Journal of Xidian University, 2010, 37(5): 862-865. (in Chinese)
[7] 程水英，张剑云. 裂变自举粒子滤波 [J]. 电子学报，2008, 36(3): 500-504.
CHENG Shuiying, ZHANG Jianyun. Fission bootstrap particle filter [J]. Acta Electronica Sinica, 2008, 36(3): 500-504. (in Chinese)
[8] 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(03): 273-277.
FANG Zheng, TONG Guofeng, XU Xinhe. Particle swarm optimized particle filter [J]. Control and Decision, 2007, 22(03): 273-277. (in Chinese)
[9] YU Yihua, ZHENG Xuanyuan. Particle filter with ant colony optimization for frequency offset estimation in OFDM systems with unknown noise distribution [J]. Signal Processing, 2011, 91(5): 1339-1342.
[10] SANJEEV M, MASKELL S. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [C]// IEEE Transactions on Signal Processing, 2002, 50:174-188.
[11] ZHANG W, LIU Y T. Adaptive particle swarm optimization for reactive power and voltage control in power systems [J]. Lecture Note in Computer Science , 2005, 3612(433): 449-452
[12] LI Ying, BAI Bendu, ZHANG Yanning. Improved particle swarm optimization algorithm for fuzzy multi-class SVM [J]. Journal of Systems Engineering and Electronics, 2010, 21(3): 509-513.
[13] 张晓绩，戴冠中，徐乃平. 遗传算法种群多样性的分析研究 [J]. 控制理论与应用， 1998, 2(1): 17-23.
ZHANG Xiaoji, DAI Guanzhong, XU Naiping. Study of diversity of population in genetic algorithm[J]. Control Theory and Application, 1998, 2(1): 17-23. (in Chinese)
[14] XU Xinyu, LI Baoxin. Adaptive Rao-Blackwellized particle filter and its evaluation for tracking in surveillance [J]. IEEE Transactions on Image Processing, 2007, 16(3): 838-849.
[15] 叶龙，王京玲，张勤. 遗传重采样粒子滤波器 [J]. 自动化学报，2007,33(8): 885-887.
YE Long, WANG Jingling, ZHANG Qin. Genetic resampling particle filter [J]. Acta Automatica Sinica, 2007, 33(8): 885-887. (in Chinese)
