一种新型自适应粒子群优化粒子滤波算法及应用

doi:10.3969/j.issn.0255-8297.2013.03.011

应用科学学报 ›› 2013, Vol. 31 ›› Issue (3): 285-293.doi: 10.3969/j.issn.0255-8297.2013.03.011

一种新型自适应粒子群优化粒子滤波算法及应用

陈志敏，薄煜明，吴盘龙，宋公飞，段文勇

南京理工大学自动化学院，南京210094

收稿日期:2011-09-14 修回日期:2011-12-20 出版日期:2013-05-28 发布日期:2013-05-28
作者简介:陈志敏，博士生，研究方向：导航制导、信息处理、控制与系统，E-mail：chenzhimin@188.com；薄煜明，研究员，博导，研究方向：火力控制、目标跟踪、导航制导，E-mail：byming@mail.njust.edu.cn
基金资助:
国防重点预研项目基金(No.40405020201)；高等学校博士学科点专项科研基金(No.20113219110027)；国家自然科学基金(No.61104196)；南京理工大学紫金之星基金(No.AB41381)资助

Novel Particle Filtering Based on Adaptive Particle Swarm Optimization and Its Application

CHEN Zhi-min, BO Yu-ming, WU Pan-long, SONG Gong-fei, DUAN Wen-yong

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2011-09-14 Revised:2011-12-20 Online:2013-05-28 Published:2013-05-28

摘要/Abstract

摘要： 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.

关键词: 粒子滤波, 粒子群优化, 目标跟踪, 故障检测

Abstract: 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.

Key words: particle filter, particle swarm optimization, target tracking, fault detection

中图分类号:

TP391

陈志敏，薄煜明，吴盘龙，宋公飞，段文勇. 一种新型自适应粒子群优化粒子滤波算法及应用[J]. 应用科学学报, 2013, 31(3): 285-293.

CHEN Zhi-min, BO Yu-ming, WU Pan-long, SONG Gong-fei, DUAN Wen-yong. Novel Particle Filtering Based on Adaptive Particle Swarm Optimization and Its Application[J]. Journal of Applied Sciences, 2013, 31(3): 285-293.

参考文献

[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)

一种新型自适应粒子群优化粒子滤波算法及应用

Novel Particle Filtering Based on Adaptive Particle Swarm Optimization and Its Application

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王光艳, 耿艳香, 陈雷. 改进粒子群算法在水下盲语音分离中的应用研究[J]. 应用科学学报, 2018, 36(4): 589-600.
[2]	姜长泓, 张永恒, 王盛慧. 基于改进粒子群优化算法的PID控制器参数优化[J]. 应用科学学报, 2017, 35(5): 667-674.
[3]	董莉, 宋晓勤, 韩杰. 基于遗传粒子群优化的认知OFDM网络资源分配算法[J]. 应用科学学报, 2017, 35(3): 288-298.
[4]	胡昭华, 王冠南, 王珏, 邵晓雯, 卞飞飞. 模板在线聚类的目标跟踪[J]. 应用科学学报, 2016, 34(4): 430-440.
[5]	胡昭华, 徐玉伟, 赵孝磊, 何军, 周游. 基于支持向量机的多特征选择目标跟踪[J]. 应用科学学报, 2015, 33(5): 502-517.
[6]	郭艳，朱方军，李宁. 认知无线电网络中的动态波束形成[J]. 应用科学学报, 2015, 33(3): 223-233.
[7]	杨洪涛，耿金华，丁小瑞，喻曹丰，禹斌. 数控机床力-几何误差的PSO-SVM建模[J]. 应用科学学报, 2014, 32(3): 325-330.
[8]	刘立昕，卞红雨. 用于水下目标跟踪的多特征融合PSOPF 算法[J]. 应用科学学报, 2013, 31(6): 564-568.
[9]	张树银，郭英，齐子森，苏令华. 共形阵列LFM信号DOA和极化参数的联合估计[J]. 应用科学学报, 2013, 31(3): 252-258.
[10]	邵洁1，董楠2. 多模块稀疏投影在密集场景目标跟踪中的应用[J]. 应用科学学报, 2013, 31(1): 104-110.
[11]	陈志敏，薄煜明，吴盘龙，刘正凡. 拟蒙特卡罗粒子滤波改进算法及其在雷达目标跟踪中的应用[J]. 应用科学学报, 2012, 30(6): 607-612.
[12]	王建青，李帆，赵建辉. 基于混合粒子群优化的巡航导弹低空突防航迹规划[J]. 应用科学学报, 2012, 30(3): 317-323.
[13]	靳一，王继武，吴乐南. 基于IM-SAPSO和SVM的EBPSK检测器设计[J]. 应用科学学报, 2012, 30(2): 141-145.
[14]	张民，陈欣，陆宇平. PSO算法用于导弹鲁棒控制器性能权函数优化[J]. 应用科学学报, 2011, 29(6): 650-654.
[15]	麻海圆1;2，孟相如1，马志强1，马森2. 多目标粒子群优化的复合型组播拥塞控制[J]. 应用科学学报, 2011, 29(5): 459-466.