粒子滤波关键技术及其应用研究
|
摘要
粒子滤波是一种基于蒙特卡罗方法和贝叶斯理论的推理算法,适用于任何可以用状态空间模型来表示的非线性非高斯系统。它具有易于编程实现,使用灵活的特点,引起了广泛的重视,已成为信号处理、人工智能、自动控制,机器学习等领域的研究热点。粒子滤波的研究仍处于起步阶段,许多关键技术如建议分布函数,重采样,收敛性分析等还未能有效解决。本文针对粒子滤波存在的权值退化,样本衰竭的问题,基于粒子滤波理论基础的学习,对粒子滤波的重采样,自适应机制和多样性测度等关键技术及其在单机动目标跟踪的应用展开研究。
本文对粒子滤波理论与算法进行深入调研,在对粒子滤波算法的实现原理和步骤的研究基础之上,从重采样的实现原理、质量和计算复杂度的角度对四种基本的重采样算法-多项式重采样、分层重采样、系统重采样和剩余重采样进行了理论分析,并通过仿真实验比较四种重采样算法的多样性和性能。接着介绍了部分重采样思想,从理论分析和数值仿真两方面对该算法在取不同权值门限及采用系统重采样的情况下,即部分系统重采样算法的性能进行了比较。
针对重采样带来的样本衰竭问题,在部分重采样的基础上,提出了一种改进算法—基于权值选择重组的重采样算法,粒子被分组后,对需要重采样的粒子进行权值优化组合,原来权值偏小的粒子经过权值优化组合后得到了提高,可以有效的缓解重采样带的来样本衰竭问题,在单机动目标跟踪的仿真下验证了算法的可行性。
针对如何有效的控制重采样的次数,提高滤波的鲁棒性,结合自适应与多样性测度机制提出了两种自适应重采样算法,自适应部分系统重采样算法和基于多样性向导的自适应重采样算法,这两种算法根据粒子权值的退化程度自适应的调整重采样的时间,减少重采样的次数,缓解样本衰竭,在单机动目标跟踪的仿真下验证了算法的有效性。
在重采样、自适应与多样性测度机制的研究成果的基础上,提出了两种基于自适应变异导向的粒子滤波改进算法,基于多样性向导的自适应变异粒子滤波和基于权值选择重组的自适应变异粒子滤波,这两种粒子滤波的改进算法在重采样后对粒子进行自适应变异,可以有效的提高滤波的多样性和估计性能。在单机动目标跟踪的仿真下验证了算法的可行性。
Particle filter is a reasoning algorithm based on Monte Carlo methods and Bayesian. It can be applied to any nonlinear non-Gaussian systems which can be represented by state space model. Particle filter is flexible and easy to program, so it attracts widely attention, it has become research focus of other fields such as the signal processing, artificial intelligence, automatic control. The study of particle filter is still in its infancy, many of the key technology such as proposal distribution, re-sampling, convergence analysis do not have effective solutions. In this paper, in view of question of the degradation and impoverishment, from the basis of particle filtering theoretical we study the key technologies of particle filter such as resampling, adaptive mechanisms, diversity measure, and single target tracking applications.
This article first conducts the deep research to the particle filtering theory and the algorithm, and introduces in detail the principle and the step of particle filter algorithm, then four kind of classical resampling algorithms - multinomial resampling, stratified resampling, systematic resampling and the residual resampling have been carried out on the theoretical analysis, and the diversity and performance of four resampling algorithm are compared through simulation. Then it introduces partial resampling algorithm, performance of the algorithms were compared from theoretical analysis and simulation when the threshold in the different weights.
For the sample impoverishment which caused by resampling, based on partial resampling, a kind of improvement algorithm -weight choose restructuring resampling based on the weight optimum are given. After the groups step ,the weight particles which need resampling are made optimum composition, the originally small weight after weight optimum composition had been enhancement, it effectively relieve sampling impoverishment question. The feasibility of algorithms are verified under the simulation of single-target tracking
For how to effectively control the number of resampling to improve the robustness of filter in this paper, two adaptive time resampling algorithm are given combined with the adaptive and diversity measure mechanism, adaptive partial systematic resampling algorithm and based on diversity guidance adaptive resampling. The two algorithms basis on the degree of the particles weight deterioration to adaptive tuning resampling time which reduces the number of reasampling and relieve sampling impoverishment question. The effectiveness of algorithms is verified under the simulation of single-target tracking.
Based on the study of rsampling, the adaptive mechanisms and diversity measure, two improved particle filtering algorithms based on adaptive mutation are propose, adaptive mutation particle filter based on diversity guidance and adaptive mutation particle filter based on weight choose restructuring resampling. Both in the two improved particle filtering algorithms, the particles after resampling add adaptive mutation step, which can effectively improve diversity and estimation performance of the filtering. The effectiveness of algorithms are verified under the simulation of single-target tracking.
本文对粒子滤波理论与算法进行深入调研,在对粒子滤波算法的实现原理和步骤的研究基础之上,从重采样的实现原理、质量和计算复杂度的角度对四种基本的重采样算法-多项式重采样、分层重采样、系统重采样和剩余重采样进行了理论分析,并通过仿真实验比较四种重采样算法的多样性和性能。接着介绍了部分重采样思想,从理论分析和数值仿真两方面对该算法在取不同权值门限及采用系统重采样的情况下,即部分系统重采样算法的性能进行了比较。
针对重采样带来的样本衰竭问题,在部分重采样的基础上,提出了一种改进算法—基于权值选择重组的重采样算法,粒子被分组后,对需要重采样的粒子进行权值优化组合,原来权值偏小的粒子经过权值优化组合后得到了提高,可以有效的缓解重采样带的来样本衰竭问题,在单机动目标跟踪的仿真下验证了算法的可行性。
针对如何有效的控制重采样的次数,提高滤波的鲁棒性,结合自适应与多样性测度机制提出了两种自适应重采样算法,自适应部分系统重采样算法和基于多样性向导的自适应重采样算法,这两种算法根据粒子权值的退化程度自适应的调整重采样的时间,减少重采样的次数,缓解样本衰竭,在单机动目标跟踪的仿真下验证了算法的有效性。
在重采样、自适应与多样性测度机制的研究成果的基础上,提出了两种基于自适应变异导向的粒子滤波改进算法,基于多样性向导的自适应变异粒子滤波和基于权值选择重组的自适应变异粒子滤波,这两种粒子滤波的改进算法在重采样后对粒子进行自适应变异,可以有效的提高滤波的多样性和估计性能。在单机动目标跟踪的仿真下验证了算法的可行性。
Particle filter is a reasoning algorithm based on Monte Carlo methods and Bayesian. It can be applied to any nonlinear non-Gaussian systems which can be represented by state space model. Particle filter is flexible and easy to program, so it attracts widely attention, it has become research focus of other fields such as the signal processing, artificial intelligence, automatic control. The study of particle filter is still in its infancy, many of the key technology such as proposal distribution, re-sampling, convergence analysis do not have effective solutions. In this paper, in view of question of the degradation and impoverishment, from the basis of particle filtering theoretical we study the key technologies of particle filter such as resampling, adaptive mechanisms, diversity measure, and single target tracking applications.
This article first conducts the deep research to the particle filtering theory and the algorithm, and introduces in detail the principle and the step of particle filter algorithm, then four kind of classical resampling algorithms - multinomial resampling, stratified resampling, systematic resampling and the residual resampling have been carried out on the theoretical analysis, and the diversity and performance of four resampling algorithm are compared through simulation. Then it introduces partial resampling algorithm, performance of the algorithms were compared from theoretical analysis and simulation when the threshold in the different weights.
For the sample impoverishment which caused by resampling, based on partial resampling, a kind of improvement algorithm -weight choose restructuring resampling based on the weight optimum are given. After the groups step ,the weight particles which need resampling are made optimum composition, the originally small weight after weight optimum composition had been enhancement, it effectively relieve sampling impoverishment question. The feasibility of algorithms are verified under the simulation of single-target tracking
For how to effectively control the number of resampling to improve the robustness of filter in this paper, two adaptive time resampling algorithm are given combined with the adaptive and diversity measure mechanism, adaptive partial systematic resampling algorithm and based on diversity guidance adaptive resampling. The two algorithms basis on the degree of the particles weight deterioration to adaptive tuning resampling time which reduces the number of reasampling and relieve sampling impoverishment question. The effectiveness of algorithms is verified under the simulation of single-target tracking.
Based on the study of rsampling, the adaptive mechanisms and diversity measure, two improved particle filtering algorithms based on adaptive mutation are propose, adaptive mutation particle filter based on diversity guidance and adaptive mutation particle filter based on weight choose restructuring resampling. Both in the two improved particle filtering algorithms, the particles after resampling add adaptive mutation step, which can effectively improve diversity and estimation performance of the filtering. The effectiveness of algorithms are verified under the simulation of single-target tracking.
引文
[1] SRIVASTAVA A, LANTERMAN A D, GRENANDER U, et al. Monte Carlo techniques for automated target recognition [M] DOUCET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Particle. New York: Springer-Verlag, 2001, 533-552.
[2] ARULAMPALAM M S, MASKELL S, GORDON N J, et al. A tutorial on particle filters for online non-1inear/non-gaussian Bayesian tracking [J]. IEEE Transaction on Signal Processing, 2002, 50(20): 174-188.
[3] KOTECHA J H. Sequential Monte Carlo methods for dynamic state space models with applications to communications [D]. New York: State University of New York, 2001.
[4]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985.
[5]裴鹿成,张孝泽.蒙特卡罗方法及其在粒子输运问题中的应用[M].北京:科学出版社,1980.
[6] HUE C, Le CADRE J P PERERZ P. Sequential Monte Carlo methods for multiple target tracking and data fusion [J]. IEEE Trans on Signal Processing, 2002, 50(2): 309-325.
[7] MCGINNITY S, IRWIN G W. Multiple model bootstrap filter for maneuvering target tracking [J]. IEEE Trans on Aerospace and Electronic System, 2000, 36(3): 1006-1012.
[8] GORDON N. A hybrid bootstrap filter for target tracking [J]. IEEE Trans on Aerospace and Electronic System, 1997, 36(1): 353-358.
[9] DOUCET A, GORDON N J, KRISHMAMURTHY V. Particle filters for state estimation of jump Markov Linear systems [J]. IEEE Trans on Signal Processing, 2001, 49(3): 613-624.
[10] GUO D, WANG X D. Dynamic sensor collaboration via sequential Monte Carlo [J]. IEEE J of Selected Areas in Communications, 2004, 22(6): 1037-1012.
[11] de FREITAS N, ANDRIEU C, HOJEN-SORENSEN P, et al. Sequential Monte Carlo methods for neural networks [M] DOUDET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Particle. New York: Springer-Verlag, 2001, 359-380.
[12] WAFD D B, LEHMANN E A, WILLOANMSON R C. Particle filtering algorithms for tracking an acoustic source in a reverberant environment [J]. IEEE Trans on Speech and Audio Processing, 2003, 11(6): 826-836.
[13] FOX D, THRUN S, BURGARE W, et al. Particle filters for mobile robot localization [M] DOUCET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Particle.New York: Springer-Verlag, 2001, 401-472.
[14] HERNANDEZ M L, KIRUBARAJAN T, BAR-SHALOM Y. Multisensor resource deployment using posterior Cramer-Rao bounds [J]. IEEE Trans on Aerospace and Electronic System, 2004, 40(2): 399-414.
[15] ORTON M, MARRS A. A Bayesian approach to multi-target tracking and data fusion with out-of- sequence measurements [J]. IEE Target Tracking: Algorithms and Applications, 2001, 1(1): 1-5.
[16] PENNY D, WILLIAMS M. A sequential approach to Multi-sensor resource management using particle filters [C]. Proceedings of SPIE on Signal and Data Processing of Small Targets. Washington: SPIE press, 2000, 4048: 598-609.
[17] MASKELL S, ROLLASON M, GORDON N, et al. Efficient particle filtering for multiple target tracking with application to tracking in structured images [C]. Proceedings of SPIE on Signal and Date Processing of Small Targets. Washington: SPIE Press, 2002, 4728: 251-262.
[18] BRUNO M G S. Bayesian methods for multiaspect tracking in mage sequences [J]. IEEE Trans on Signal Processing, 2004, 52(7): 1848-1861.
[19] ZOTKIN D, DURAISWAMI R, DAVIDS L S. Multimodal 3-D tracking and event detection via particle filter [C] Proceedings of IEEE Workshop on Detection and Recognition of Events in Video Canada: IEEE Press, 2001, 8: 20-27.
[20] VERMAAK J, GANGNET M, BLAKE A, etal. Sequential Monte Carlo fusion of sound and vision for speaker tracking [C] Proceedings of Small Targets. Washington: SPIE Press, 2002, 4728: 251-262.
[21] DJURIC P M, KONTECHA J H, ZHANG J, et al. Particle filtering: a review of the theory and how it can be used for solving problems in wireless communication [J]. IEEE Signal Processing Magazine, 2003, 20(5): 19-38.
[22] ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking [J]. International Journal of Computer Vision, 1998, 29(1): 5-28.
[23] CHIB S, NARDARI F, SHEPHARD N. Markov Chain Monte Carlo methods for stochastic volatility models [J]. Journal of Econometrics, 2002, 8(1): 281-316.
[24] MONTEMERLO M, THRUN S, KOLLER D, et al. FastSLAM: A factored solution to simultaneous localization and mapping problem: Proceedings of the National Conference on Artificial Intelligence[C]. Edmonton, Canada: AAAI Press, 2002: 593-598.
[25] VERMA V, GORDON G, SIMMONS R, et al. Particle filters for rover fault diagnosis [J]. IEEE Robotics & Automation Magazine special issue on Human Centered Robotics and Dependability, 2004, 11(2): 1-4.
[26] GORDON N J, SALMOND D J, EWING C. Bayesian state estimation for tracking guidance using the bootstrap filter [J].Journal of Guidance, Control and Dynamics, 1995, 1 8(6): 1434-1443.
[27] GORDON N J, SALMOND D J, SMITH A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation [J]. IEE Proceedings on Radar and Signal Processing, 1993, 140(2): 107-113.
[28] ROSENBLUTH M N, ROSENBOUTH A W. Monte Carlo calculation of the average extension of molecular chains [J]. Journal Chemical Physics, 1955, 23: 356-359.
[29] HAMMERSLEY J M, MORTON K M. Poor man’s Monte Carlo [J]. Journal Royal Statistical Society B, 1954, 16: 23-38.
[30] HANDSCHIN J E, MAYNE D Q. Monte Carlo techniques to estimate the condition expectation in multi-stage non-linear filtering [J]. International Journal control, 1969, 9(5): 547-559.
[31] ZARITAKII V S, SVETNIK V B, SHIMELEVICH L I. Monte–carlo techniques in problems of optimal information processing [J]. Automation and Remote Control, 1975, 36(3): 2015-2022.
[32] DOUCET A, de FMITAS J, GONDON N. Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag. 2001.
[33] BEADLE E R, DJUDC P M. Fast weighted bootstrap filter for non-linear state estimation [J]. IEEE Transactions on Aerosp Electrical System, 1997, 33(1): 338-343.
[34] DOUCET A, de FREITAS N, et al. The Unscented Particle Filter [EB/OL]. London: Cambridge University, 2002, http: //citeseer.is.psu.edu/325754.html.
[35] PITT M K, SHEPHARD N, Filtering via simulation: auxiliary particle filters [J]. Journal of the American Statistical Association, 1999, 94(446): 590-599.
[36] MUSSO C, OUDJANEN, LEGLAND F. Improving regularised particle filters [M]. DOUCET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Practice. New York, USA: Springer Verlag, 2001: 247-272.
[37] KOTECHA J, DJURICP M. Gaussian particle filtering [J]. IEEE Trans on Signal Processing, 2003, 51(10): 2592-2601.
[38] WEST M. Mixture models, Monte Carlo, Bayesian updating and dynamic models [J]. Computing Science and Statistics, 1993, 24: 325-333.
[39] GRISETTI G, STACHNISS C, BURGARD W. Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling: Proceedings of the IEEE International Conference on Robotics and Automation[C]. Barcelona, Spain: IEEE Press, 2005: 2443-2448.
[40] BEESON P, MURARKA A, KUIPERS B. Adapting proposal distributions for accurate, efficient mobile robot localization: Proceedings of the IEEE International Conference on Robotics and Automation[C]. Orlando, USA: IEEE Press, 2006: 49-55.
[41] KOLLER D, FRATKINA R. Using learning for approximation in stochastic processes: Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98) [C]. San Francisco, USA: Morgan Kaufmann Publishers Inc, 1998: 287-295.
[42] FOX D. KLD-Sampling: Adaptive particle filters [J]. Advances in Neural Information Processing Systems, 2001, 14(1): 713– 720.
[43] KARLSSON R, GUSTAFSSON F. Monte Carlo data association for multiple target tracking [J]. IEE Target Tracking: Algorithms and Applications, 2001, 1: 13/1-13/5.
[44] GRISETTI G, STACHNISS C, BURGARD W. Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling: Proceedings of the IEEE International Conference on Robotics and Automation[C]. Barcelona, Spain: IEEE Press, 2005: 2443-2448.
[45] LI Maohai, HONG Bingrong, CAI Zesu, et al. Novel Rao-Blackwellized Particle Filter for Mobile Robot SLAM Using Monocular Vision [J]. International Journal of Intelligent Technology, 2006, 1(1): 63-69.
[46] SULLIVAN J, CAMPBELL M, LIPSON H. Particle filters as exploration tools for autonomous rovers: Proceedings of the AIAA Guidance, Navigation and Control Conference[C]. San Francisco, USA: AIAA Press, 2005: 6447-6458.
[47] LENSER S, VELOSO M. Sensor Resetting Localization for Poorly Modelled Mobile Robots: Proceedings of the IEEE International Conference on Robotics and Automation[C]. San Francisco: IEEE Press, 2000: 1225-1232.
[48]高庆吉,雷亚莉,胡丹丹,等.基于自适应感知复位算法的移动机器人定位[J].电子学报, 2007, 35(11): 2166-2171.
[49]段琢华,蔡自兴,于金霞.移动机器人软故障检测与补偿的自适应粒子滤波算法[J].中国科学E辑:信息科学, 2008, 38(4): 565-578.
[50]厉茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建[J].吉林大学学报(工学版), 2007, 37(2): 401-406.
[51] DUAN Zuohua, CAI Zixing, YU Jinxia. Fuzzy adaptive particle filter algorithm for mobile robot fault diagnosis: Lecture Notes in Computer Science (ICONIP2006) [C]. Heidelberg, Germany: Springer Verlag, 2006: 711-720.
[52] BOLIC M., DJURIC P M, HONG. S. J. New resampling algorithms for particle filters: Proceedings of the 2003 International Conference on Acoustics, Speech, and Signal Processing (ICASSP2003) [C]. HongKong: IEEE press, 2003, 589-592.
[53]王健,金永镐,董华春.基于新的采样更新方法的粒子滤波算法[J].系统工程与电子技术, 2008, 30(6): 1148-1150.
[54]张琪,胡昌华,乔玉坤.基于权值选择的粒子滤波算法研究[J].控制与决策,2008,23(1): 117-120.
[55]高显忠,赵伟,侯中喜.粒子滤波改进算法研究[J].弹箭与制导学报,2009,29(3): 241-244.
[56]裴福俊,孙新,刘红云等.均匀重采样粒子滤波器在SINS初始对准中的应用[J].北京工业大学学报, 2008, 34(6): 567-571.
[57]邹国辉,敬忠梁,胡洪涛.基于优化组合的重采样粒子滤波算法[J].上海交通大学学报, 2006, 40(7): 1135-1139.
[58]李彦翔,刘庆华.高斯条件下基于粒子滤波的声源定位[J].电声技术,2009,33(10):52-55.
[59]姚军,蒋晓瑜,登崇等.用粒子滤波器实现电子稳像[J].光学精密工程,2009,17(5):1105-1110.
[60]张高煜,褚少鹤.基于粒子滤波的高频金融时问序列预测[J].现代电子技术,2009,18(305):117-120.
[61] KWAK N, Analysis of Resampling Process for thep Particle Depletion Problem in FastSLAM :Bolic M., Djuric P M, and Hong. S. J. New resampling algorithms for particle filters: Proceedings of the 16th International Conference on Robot&Human Interactive Comunication,) [C]. Korea, 2007, 200-205
[62] SMITH A F M, GELFANDA E. Bayesian statistics without tears: A sampling-resampling respective [J]. American Statistician, 1992, 46(2): 84-88.
[63]杜正聪.粒子滤波及其在MIMO无线通信中的应用研究[D].成都:电子科技大学,2008.
[64]冯弛,王萌,汲清波.粒子滤波器的重采样算法的分析与比较[J].系统仿真学报,2009, 21(4): 1101-1105.
[65]于金霞,蔡自兴,段琢华.基于粒子滤波的移动机器人定位关键技术研究综述[J].计算机研究应用,2007, 24(11): 9-14.
[66] HOL J, SCHON T B, GUSTAFSSON F. On resampling algorithms for particle filters [C] Proceedings of Nonlinear Statistical Signal Processing Workshop, UK: [s.n.], 2006: 79-82.
[67] DOUC R, CAPPE O, MOULENES E. Comparison of resampling schemes for particle filtering [C]. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. Zagreb: [s.n.], 2005: 64-69.
[68] HINES W. W, MONTGOMERY D. C. Probability and statistics in engineering and management science [M]. John Wiley & Sons, New York: 1972.
[69]谌剑,严平,张静远.权值优化组合粒子滤波算法研究[J].计算机工程与应用,2009,45(24): 33-39.
[70] LIU J S. Metropolized independent sampling with comparisons to rejection sampling and importance sampling [J]. Statistics and Computing, 1996, 6(1): 113-119.
[71] SULLIVAN J, CAMPBELL M, LIPSONN H. Particle filters as exploration tools for autonomous rovers: Proceedings of the AIAA Guidance, Navigation and Control Conference[C]. San Francisco, USA: AIAA Press, 2005: 6447-6458.
[72] FOX D. Adapting the sample size in particle filters through KLD-sampling [J]. International Journal of Robotics Research, 2003, 22(12): 985-1003.
[73]杨振强,王常虹,庄显义.自适应复制交叉和突变的遗传算法[J].电子科学学刊. 2000, 22(1): 112-117.
[74]段琢华.基于自适应粒子滤波器的移动机器人故障诊断理论与方法研究[D].长沙模式识别与智能系统,中南大学, 2007.
[75] JIAO W. LIU G. B. Particle swarm optimization algorithm based on diversity feedback [J]. Computer Engineering, 2009.35(.22) 202-204.
[2] ARULAMPALAM M S, MASKELL S, GORDON N J, et al. A tutorial on particle filters for online non-1inear/non-gaussian Bayesian tracking [J]. IEEE Transaction on Signal Processing, 2002, 50(20): 174-188.
[3] KOTECHA J H. Sequential Monte Carlo methods for dynamic state space models with applications to communications [D]. New York: State University of New York, 2001.
[4]徐钟济.蒙特卡罗方法[M].上海:上海科学技术出版社,1985.
[5]裴鹿成,张孝泽.蒙特卡罗方法及其在粒子输运问题中的应用[M].北京:科学出版社,1980.
[6] HUE C, Le CADRE J P PERERZ P. Sequential Monte Carlo methods for multiple target tracking and data fusion [J]. IEEE Trans on Signal Processing, 2002, 50(2): 309-325.
[7] MCGINNITY S, IRWIN G W. Multiple model bootstrap filter for maneuvering target tracking [J]. IEEE Trans on Aerospace and Electronic System, 2000, 36(3): 1006-1012.
[8] GORDON N. A hybrid bootstrap filter for target tracking [J]. IEEE Trans on Aerospace and Electronic System, 1997, 36(1): 353-358.
[9] DOUCET A, GORDON N J, KRISHMAMURTHY V. Particle filters for state estimation of jump Markov Linear systems [J]. IEEE Trans on Signal Processing, 2001, 49(3): 613-624.
[10] GUO D, WANG X D. Dynamic sensor collaboration via sequential Monte Carlo [J]. IEEE J of Selected Areas in Communications, 2004, 22(6): 1037-1012.
[11] de FREITAS N, ANDRIEU C, HOJEN-SORENSEN P, et al. Sequential Monte Carlo methods for neural networks [M] DOUDET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Particle. New York: Springer-Verlag, 2001, 359-380.
[12] WAFD D B, LEHMANN E A, WILLOANMSON R C. Particle filtering algorithms for tracking an acoustic source in a reverberant environment [J]. IEEE Trans on Speech and Audio Processing, 2003, 11(6): 826-836.
[13] FOX D, THRUN S, BURGARE W, et al. Particle filters for mobile robot localization [M] DOUCET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Particle.New York: Springer-Verlag, 2001, 401-472.
[14] HERNANDEZ M L, KIRUBARAJAN T, BAR-SHALOM Y. Multisensor resource deployment using posterior Cramer-Rao bounds [J]. IEEE Trans on Aerospace and Electronic System, 2004, 40(2): 399-414.
[15] ORTON M, MARRS A. A Bayesian approach to multi-target tracking and data fusion with out-of- sequence measurements [J]. IEE Target Tracking: Algorithms and Applications, 2001, 1(1): 1-5.
[16] PENNY D, WILLIAMS M. A sequential approach to Multi-sensor resource management using particle filters [C]. Proceedings of SPIE on Signal and Data Processing of Small Targets. Washington: SPIE press, 2000, 4048: 598-609.
[17] MASKELL S, ROLLASON M, GORDON N, et al. Efficient particle filtering for multiple target tracking with application to tracking in structured images [C]. Proceedings of SPIE on Signal and Date Processing of Small Targets. Washington: SPIE Press, 2002, 4728: 251-262.
[18] BRUNO M G S. Bayesian methods for multiaspect tracking in mage sequences [J]. IEEE Trans on Signal Processing, 2004, 52(7): 1848-1861.
[19] ZOTKIN D, DURAISWAMI R, DAVIDS L S. Multimodal 3-D tracking and event detection via particle filter [C] Proceedings of IEEE Workshop on Detection and Recognition of Events in Video Canada: IEEE Press, 2001, 8: 20-27.
[20] VERMAAK J, GANGNET M, BLAKE A, etal. Sequential Monte Carlo fusion of sound and vision for speaker tracking [C] Proceedings of Small Targets. Washington: SPIE Press, 2002, 4728: 251-262.
[21] DJURIC P M, KONTECHA J H, ZHANG J, et al. Particle filtering: a review of the theory and how it can be used for solving problems in wireless communication [J]. IEEE Signal Processing Magazine, 2003, 20(5): 19-38.
[22] ISARD M, BLAKE A. Condensation-conditional density propagation for visual tracking [J]. International Journal of Computer Vision, 1998, 29(1): 5-28.
[23] CHIB S, NARDARI F, SHEPHARD N. Markov Chain Monte Carlo methods for stochastic volatility models [J]. Journal of Econometrics, 2002, 8(1): 281-316.
[24] MONTEMERLO M, THRUN S, KOLLER D, et al. FastSLAM: A factored solution to simultaneous localization and mapping problem: Proceedings of the National Conference on Artificial Intelligence[C]. Edmonton, Canada: AAAI Press, 2002: 593-598.
[25] VERMA V, GORDON G, SIMMONS R, et al. Particle filters for rover fault diagnosis [J]. IEEE Robotics & Automation Magazine special issue on Human Centered Robotics and Dependability, 2004, 11(2): 1-4.
[26] GORDON N J, SALMOND D J, EWING C. Bayesian state estimation for tracking guidance using the bootstrap filter [J].Journal of Guidance, Control and Dynamics, 1995, 1 8(6): 1434-1443.
[27] GORDON N J, SALMOND D J, SMITH A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation [J]. IEE Proceedings on Radar and Signal Processing, 1993, 140(2): 107-113.
[28] ROSENBLUTH M N, ROSENBOUTH A W. Monte Carlo calculation of the average extension of molecular chains [J]. Journal Chemical Physics, 1955, 23: 356-359.
[29] HAMMERSLEY J M, MORTON K M. Poor man’s Monte Carlo [J]. Journal Royal Statistical Society B, 1954, 16: 23-38.
[30] HANDSCHIN J E, MAYNE D Q. Monte Carlo techniques to estimate the condition expectation in multi-stage non-linear filtering [J]. International Journal control, 1969, 9(5): 547-559.
[31] ZARITAKII V S, SVETNIK V B, SHIMELEVICH L I. Monte–carlo techniques in problems of optimal information processing [J]. Automation and Remote Control, 1975, 36(3): 2015-2022.
[32] DOUCET A, de FMITAS J, GONDON N. Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag. 2001.
[33] BEADLE E R, DJUDC P M. Fast weighted bootstrap filter for non-linear state estimation [J]. IEEE Transactions on Aerosp Electrical System, 1997, 33(1): 338-343.
[34] DOUCET A, de FREITAS N, et al. The Unscented Particle Filter [EB/OL]. London: Cambridge University, 2002, http: //citeseer.is.psu.edu/325754.html.
[35] PITT M K, SHEPHARD N, Filtering via simulation: auxiliary particle filters [J]. Journal of the American Statistical Association, 1999, 94(446): 590-599.
[36] MUSSO C, OUDJANEN, LEGLAND F. Improving regularised particle filters [M]. DOUCET A, de FREITAS J F G, GORDON N J. Sequential Monte Carlo Methods in Practice. New York, USA: Springer Verlag, 2001: 247-272.
[37] KOTECHA J, DJURICP M. Gaussian particle filtering [J]. IEEE Trans on Signal Processing, 2003, 51(10): 2592-2601.
[38] WEST M. Mixture models, Monte Carlo, Bayesian updating and dynamic models [J]. Computing Science and Statistics, 1993, 24: 325-333.
[39] GRISETTI G, STACHNISS C, BURGARD W. Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling: Proceedings of the IEEE International Conference on Robotics and Automation[C]. Barcelona, Spain: IEEE Press, 2005: 2443-2448.
[40] BEESON P, MURARKA A, KUIPERS B. Adapting proposal distributions for accurate, efficient mobile robot localization: Proceedings of the IEEE International Conference on Robotics and Automation[C]. Orlando, USA: IEEE Press, 2006: 49-55.
[41] KOLLER D, FRATKINA R. Using learning for approximation in stochastic processes: Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98) [C]. San Francisco, USA: Morgan Kaufmann Publishers Inc, 1998: 287-295.
[42] FOX D. KLD-Sampling: Adaptive particle filters [J]. Advances in Neural Information Processing Systems, 2001, 14(1): 713– 720.
[43] KARLSSON R, GUSTAFSSON F. Monte Carlo data association for multiple target tracking [J]. IEE Target Tracking: Algorithms and Applications, 2001, 1: 13/1-13/5.
[44] GRISETTI G, STACHNISS C, BURGARD W. Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling: Proceedings of the IEEE International Conference on Robotics and Automation[C]. Barcelona, Spain: IEEE Press, 2005: 2443-2448.
[45] LI Maohai, HONG Bingrong, CAI Zesu, et al. Novel Rao-Blackwellized Particle Filter for Mobile Robot SLAM Using Monocular Vision [J]. International Journal of Intelligent Technology, 2006, 1(1): 63-69.
[46] SULLIVAN J, CAMPBELL M, LIPSON H. Particle filters as exploration tools for autonomous rovers: Proceedings of the AIAA Guidance, Navigation and Control Conference[C]. San Francisco, USA: AIAA Press, 2005: 6447-6458.
[47] LENSER S, VELOSO M. Sensor Resetting Localization for Poorly Modelled Mobile Robots: Proceedings of the IEEE International Conference on Robotics and Automation[C]. San Francisco: IEEE Press, 2000: 1225-1232.
[48]高庆吉,雷亚莉,胡丹丹,等.基于自适应感知复位算法的移动机器人定位[J].电子学报, 2007, 35(11): 2166-2171.
[49]段琢华,蔡自兴,于金霞.移动机器人软故障检测与补偿的自适应粒子滤波算法[J].中国科学E辑:信息科学, 2008, 38(4): 565-578.
[50]厉茂海,洪炳熔,罗荣华.用改进的Rao-Blackwellized粒子滤波器实现移动机器人同时定位和地图创建[J].吉林大学学报(工学版), 2007, 37(2): 401-406.
[51] DUAN Zuohua, CAI Zixing, YU Jinxia. Fuzzy adaptive particle filter algorithm for mobile robot fault diagnosis: Lecture Notes in Computer Science (ICONIP2006) [C]. Heidelberg, Germany: Springer Verlag, 2006: 711-720.
[52] BOLIC M., DJURIC P M, HONG. S. J. New resampling algorithms for particle filters: Proceedings of the 2003 International Conference on Acoustics, Speech, and Signal Processing (ICASSP2003) [C]. HongKong: IEEE press, 2003, 589-592.
[53]王健,金永镐,董华春.基于新的采样更新方法的粒子滤波算法[J].系统工程与电子技术, 2008, 30(6): 1148-1150.
[54]张琪,胡昌华,乔玉坤.基于权值选择的粒子滤波算法研究[J].控制与决策,2008,23(1): 117-120.
[55]高显忠,赵伟,侯中喜.粒子滤波改进算法研究[J].弹箭与制导学报,2009,29(3): 241-244.
[56]裴福俊,孙新,刘红云等.均匀重采样粒子滤波器在SINS初始对准中的应用[J].北京工业大学学报, 2008, 34(6): 567-571.
[57]邹国辉,敬忠梁,胡洪涛.基于优化组合的重采样粒子滤波算法[J].上海交通大学学报, 2006, 40(7): 1135-1139.
[58]李彦翔,刘庆华.高斯条件下基于粒子滤波的声源定位[J].电声技术,2009,33(10):52-55.
[59]姚军,蒋晓瑜,登崇等.用粒子滤波器实现电子稳像[J].光学精密工程,2009,17(5):1105-1110.
[60]张高煜,褚少鹤.基于粒子滤波的高频金融时问序列预测[J].现代电子技术,2009,18(305):117-120.
[61] KWAK N, Analysis of Resampling Process for thep Particle Depletion Problem in FastSLAM :Bolic M., Djuric P M, and Hong. S. J. New resampling algorithms for particle filters: Proceedings of the 16th International Conference on Robot&Human Interactive Comunication,) [C]. Korea, 2007, 200-205
[62] SMITH A F M, GELFANDA E. Bayesian statistics without tears: A sampling-resampling respective [J]. American Statistician, 1992, 46(2): 84-88.
[63]杜正聪.粒子滤波及其在MIMO无线通信中的应用研究[D].成都:电子科技大学,2008.
[64]冯弛,王萌,汲清波.粒子滤波器的重采样算法的分析与比较[J].系统仿真学报,2009, 21(4): 1101-1105.
[65]于金霞,蔡自兴,段琢华.基于粒子滤波的移动机器人定位关键技术研究综述[J].计算机研究应用,2007, 24(11): 9-14.
[66] HOL J, SCHON T B, GUSTAFSSON F. On resampling algorithms for particle filters [C] Proceedings of Nonlinear Statistical Signal Processing Workshop, UK: [s.n.], 2006: 79-82.
[67] DOUC R, CAPPE O, MOULENES E. Comparison of resampling schemes for particle filtering [C]. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. Zagreb: [s.n.], 2005: 64-69.
[68] HINES W. W, MONTGOMERY D. C. Probability and statistics in engineering and management science [M]. John Wiley & Sons, New York: 1972.
[69]谌剑,严平,张静远.权值优化组合粒子滤波算法研究[J].计算机工程与应用,2009,45(24): 33-39.
[70] LIU J S. Metropolized independent sampling with comparisons to rejection sampling and importance sampling [J]. Statistics and Computing, 1996, 6(1): 113-119.
[71] SULLIVAN J, CAMPBELL M, LIPSONN H. Particle filters as exploration tools for autonomous rovers: Proceedings of the AIAA Guidance, Navigation and Control Conference[C]. San Francisco, USA: AIAA Press, 2005: 6447-6458.
[72] FOX D. Adapting the sample size in particle filters through KLD-sampling [J]. International Journal of Robotics Research, 2003, 22(12): 985-1003.
[73]杨振强,王常虹,庄显义.自适应复制交叉和突变的遗传算法[J].电子科学学刊. 2000, 22(1): 112-117.
[74]段琢华.基于自适应粒子滤波器的移动机器人故障诊断理论与方法研究[D].长沙模式识别与智能系统,中南大学, 2007.
[75] JIAO W. LIU G. B. Particle swarm optimization algorithm based on diversity feedback [J]. Computer Engineering, 2009.35(.22) 202-204.