Particle filters for HMM state inference

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (8): 161-163.

• 数据库、信号与信息处理 • Previous Articles Next Articles

Particle filters for HMM state inference

ZHU Chengwen1, LI Bing2, HU Kui3, PANG Kui2

1.Wuhan Ordnance N.C.O Academy PLA, Wuhan 430075, China
2.College of Science, National University of Defense Technology, Changsha 410073, China
3.96 Division, Unit 92941 of PLA, China

Received:1900-01-01 Revised:1900-01-01 Online:2012-03-11 Published:2012-03-11

HMM隐状态的粒子滤波估计

朱成文1，李兵2，胡奎3，庞魁2

1.武汉军械士官学校，武汉 430075
2.国防科技大学理学院，长沙 410073
3.92941部队 96分队

Abstract

Abstract: A problem of hidden-state-estimating is always attained when Hidden Markov Model（HMM） is practically applied. Traditional Viterbi algorithm cannot be generally effective, however, the particle filters, employing a group of weighted samples to approximate the optimum estimation of state, is suitable for non-linear and non-Gaussian problems. Incorporating the characteristics of particle filters, it presents the hidden state estimation algorithm based on SISR. Experimental results show the predominance of the method over the Viterbi algorithm.

Key words: hidden Markov models, particle filters, instrumental distribution, resampling

摘要： 利用隐马氏模型解决实际问题时，其最终目的往往是隐状态估计问题。传统的Viterbi算法适用范围有限，而粒子滤波通过一组加权样本逼近状态的最优估计，适用于任意非线性、非高斯动态系统状态估计问题。利用粒子滤波的优点，提出了基于SISR的HMM隐状态估计算法，仿真结果表明，该方法比Viterbi算法有更高的估计精度。

关键词: 隐马氏模型, 粒子滤波, 建议分布, 重抽样

ZHU Chengwen1, LI Bing2, HU Kui3, PANG Kui2. Particle filters for HMM state inference[J]. Computer Engineering and Applications, 2012, 48(8): 161-163.

朱成文1，李兵2，胡奎3，庞魁2. HMM隐状态的粒子滤波估计[J]. 计算机工程与应用, 2012, 48(8): 161-163.

[1]	GU Zhaojun, WU You, ZHAO Chundi, ZHOU Jingxian. Resampling and Boosting Techniques for Balanced Traffic Classification [J]. Computer Engineering and Applications, 2020, 56(6): 86-91.
[2]	XU Lingling, CHI Dongxiang. Machine Learning Classification Strategy for Imbalanced Data Sets [J]. Computer Engineering and Applications, 2020, 56(24): 12-27.
[3]	WANG Xiaohua, NIE Tengteng. Research on Optimized Particle Filtering by Improved Cuckoo Algorithm [J]. Computer Engineering and Applications, 2020, 56(12): 60-65.
[4]	HOU Beibei, LIU Sanyang, PU Shiye. Imbalanced Data Classification Method Based on Boundary Mixed Resampling [J]. Computer Engineering and Applications, 2020, 56(1): 46-52.
[5]	ZAN Meng’en, ZHOU Hang, HAN Dan, YANG Gang, XU Guoliang. Survey of Particle Filter Target Tracking Algorithms [J]. Computer Engineering and Applications, 2019, 55(5): 8-17.
[6]	LI Ying1, YANG Qiuxiang1, LEI Haiwei2, DU Bo2. Improved forward and reverse wraping depth image-based rendering [J]. Computer Engineering and Applications, 2018, 54(5): 186-190.
[7]	MA Zhenghua1, LI Lei2, QIAO Yutao2, RONG Hailong3, CAO Haiting2. Dynamic gesture recognition research and analysis based on multi-sensor fusion [J]. Computer Engineering and Applications, 2017, 53(17): 153-159.
[8]	XIAO Xiaohong1, ZHANG Yi2, LIU Dongsheng1, OUYANG Chunjuan1. Music classification based on Hidden Markov Models [J]. Computer Engineering and Applications, 2017, 53(16): 138-143.
[9]	YANG Mingxia1，3, WANG Wanliang1，2, SHAO Pengfei1，4. Target classification based on hidden Markov model in Wireless Sensor Networks [J]. Computer Engineering and Applications, 2015, 51(22): 228-232.
[10]	LIU Xuedong, ZHANG Kang, YANG Jie. Camera motion parameters estimation based on particle filter [J]. Computer Engineering and Applications, 2014, 50(7): 144-148.
[11]	XU Hui1, Riyiman TURSUN1，2, Wushour SILAMU2. Online-handwriting recognition research of Uyghur word using GMM and HMM [J]. Computer Engineering and Applications, 2014, 50(11): 202-205.
[12]	LI Rui, MAO Li, ZHANG Jiurui. Particle filter resampling based on chaos immunity genetic optimization [J]. Computer Engineering and Applications, 2013, 49(6): 209-212.
[13]	WU Lei1, FANG Bin1, DIAO Liping2, CHEN Jing1, XIE Nana1. Imbalanced data resampling based on oversampling and under-sampling [J]. Computer Engineering and Applications, 2013, 49(21): 172-176.
[14]	XIE Nana, FANG Bin, WU Lei. Study of text categorization on imbalanced data [J]. Computer Engineering and Applications, 2013, 49(20): 118-121.
[15]	ZHANG Hang1, LI Mengli2, YANG Qingbo2. Particle filter algorithm based on niching genetic algorithm [J]. Computer Engineering and Applications, 2013, 49(18): 191-194.

Particle filters for HMM state inference

HMM隐状态的粒子滤波估计

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics