变结构交互式多模型滤波和平滑算法

doi:10.12305/j.issn.1001-506X.2023.12.31

Abstract

Abstract:

A variable structure interactive multiple model filtering and smoothing algorithm is proposed for maneuvering target tracking. Firstly, the filtering and smoothing problems are described briefly, and the mathematical models of forward interactive multiple model filtering and backward interactive multiple model smoothing are given. Then, a new accurate model of the variable-structure interactive multiple model algorithm is established. The model subsets run independently in parallel, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Finally, the filtering data of the variable structure interactive multiple model algorithm is smoothed, and the variable structure interactive multiple model filtering and smoothing algorithm is obtained, which further improves the target tracking accuracy due to the combination of forward filtering and backward smoothing. Simulation results show that the tracking performance of the variable-structure interactive multiple model filtering and smoothing algorithm is better than other methods.

Key words: variable structure interactive multiple model, maneuvering target tracking, filtering, smoothing

CLC Number:

TP391

Weiyi CHEN, Fan HE, Guoqiang LIU, Weiwei MAO. Variable structure interactive multiple model filtering and smoothing algorithm[J]. Systems Engineering and Electronics, 2023, 45(12): 4005-4012.

Figures/Tables 11

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References 30

1	CAO W , WANG S L , FERNANDEZ C , et al. A novel adaptive state of charge estimation method of full life cycling lithium-ion batteries based on the multiple parameter optimization[J]. Energy Science & Engineering, 2019, 7 (5): 1544- 1556.
2	WANG S , FERNANDEZ C , FAN Y , et al. A novel safety assurance method based on the compound equivalent modeling and iterate reduce particle-adaptive Kalman filtering for the unmanned aerial vehicle lithium ion batteries[J]. Energy Science & Engineering, 2020, 8 (5): 1484- 1500.
3	WANG S L , JIN S Y , BAI D K , et al. A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries[J]. Energy Reports, 2021, 7, 5562- 5574. doi: 10.1016/j.egyr.2021.08.182
4	XU C , WANG X X , DUAN S H , et al. Spatial-temporal constrained particle filter for cooperative target tracking[J]. Journal of network and computer applications, 2021, 176, 102913. doi: 10.1016/j.jnca.2020.102913
5	郑天宇. 基于循环神经网络的临近空间高超声速目标航迹估计与预报[D]. 哈尔滨: 哈尔滨工业大学, 2020.
	ZHENG T Y. Trajectory estimation and prediction for near space hypersonic targets via recurrent neural networks[D]. Harbin: Harbin Institute of Technology, 2020.
6	BLOM H A , BAR-SHALOM Y . The interacting multiple model algorithm for systems with Markovian switching coefficients[J]. IEEE trans.on Automatic Control, 1988, 33 (8): 780- 783. doi: 10.1109/9.1299
7	LIN H J, ATHERTON D. Investigation of IMM tracking algorithm for the maneuvering target tracking[C]//Proc. of the IEEE Regional Conference on Aerospace Control Systems, 1993.
8	GAO H B , QIN Y C , HU C , et al. An interacting multiple model for trajectory prediction of intelligent vehicles in typical road traffic scenario[J]. IEEE Trans.on Neural Networks and Learning Systems, 2023, 34 (9): 6468- 6479. doi: 10.1109/TNNLS.2021.3136866
9	LEE Y L , CHEN Y W . IMM estimator based on fuzzy weighted input estimation for tracking a maneuvering target[J]. Applied mathematical modelling, 2015, 39 (19): 5791- 5802. doi: 10.1016/j.apm.2015.02.031
10	FARRELL W J . Interacting multiple model filter for tactical ballistic missile tracking[J]. IEEE Trans.on Aerospace and Electronic Systems, 2008, 44 (2): 418- 426. doi: 10.1109/TAES.2008.4560196
11	AVERBUCH A , ITZIKOWITZ S , KAPON T . Radar target tracking-Viterbi versus IMM[J]. IEEE Trans.on Aerospace and Electronic Systems, 1991, 27 (3): 550- 563. doi: 10.1109/7.81437
12	MUNIR A , ATHERTON D . Adaptive interacting multiple model algorithm for tracking a manoeuvring target[J]. IEE Proceedings-Radar, Sonar and Navigation, 1995, 142 (1): 11- 17. doi: 10.1049/ip-rsn:19951528
13	LAN J , LI X R , JILKOV V P , et al. Second-order Markov chain based multiple-model algorithm for maneuvering target tracking[J]. IEEE Trans.on Aerospace and Electronic Systems, 2013, 49 (1): 3- 19. doi: 10.1109/TAES.2013.6404088
14	JIN X B , ROBERT JEREMIAH R J , SU T L , et al. The new trend of state estimation: from model-driven to hybrid-driven methods[J]. Sensors, 2021, 21 (6): 2085. doi: 10.3390/s21062085
15	ZHANG J Q, ZHANG X S, SONG J Q. Maneuvering target tracking algorithm based on multiple models in radar network-ing[C]//Proc. of the 2019 International Conference on Control, Automation and Information Sciences, 2019.
16	LI S , FANG H J , SHI B . Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression[J]. Reliability Engineering & System Safety, 2021, 210, 107542.
17	LI X R , ZHANG Y . Multiple-model estimation with variable structure. V. Likely-model set algorithm[J]. IEEE Trans.on Aerospace and Electronic Systems, 2000, 36 (2): 448- 466. doi: 10.1109/7.845222
18	AL-NUAIMI M N , AL SAWAFI O S , MALIK S I , et al. Extending the unified theory of acceptance and use of technology to investigate determinants of acceptance and adoption of learning management systems in the post-pandemic era: a structural equation modeling approach[J]. Interactive Learning Environments, 2022, 1- 27.
19	QU H Q , PANG L P , LI S H . A novel interacting multiple model algorithm[J]. Signal Processing, 2009, 89 (11): 2171- 2177. doi: 10.1016/j.sigpro.2009.04.033
20	SUN L F , ZHANG J J , YU H F , et al. Tracking of maneuver-ing extended target using modified variable structure multiple-model based on adaptive grid best model augmentation[J]. Remote Sensing, 2022, 14 (7): 1613. doi: 10.3390/rs14071613
21	KONG X Y , ZHANG X P , ZHANG X Y , et al. Adaptive dynamic state estimation of distribution network based on interacting multiple model[J]. IEEE Trans.on Sustainable Energy, 2021, 13 (2): 643- 652.
22	RABBY M F , TU Y , HOSSEN M I , et al. Stacked LSTM based deep recurrent neural network with Kalman smoothing for blood glucose prediction[J]. BMC Medical Informatics and Decision Making, 2021, 21, 101. doi: 10.1186/s12911-021-01462-5
23	LI X R , JILKOV V P . Survey of maneuvering target tracking. Part Ⅰ: dynamic models[J]. IEEE Trans.on Aerospace and Electronic Systems, 2003, 39 (4): 1333- 1364. doi: 10.1109/TAES.2003.1261132
24	WATSON G A, BLAIR W D. IMM algorithm for tracking targets that maneuver through coordinated turns[C]//Proc. of the Signal and Data Processing of Small Targets, 1992.
25	ZHAN R H, WAN J W. Passive maneuvering target tracking using 3D constant-turn model[C]//Proc. of the IEEE Conference on Radar, 2006.
26	ZHANG Z R , HUANG C Q , DING D , et al. Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking[J]. Nonlinear Dynamics, 2019, 97 (2): 1227- 1243. doi: 10.1007/s11071-019-05043-0
27	GAN Z , LI B , NERETIN E S , et al. UAV maneuvering target tracking based on deep reinforcement learning[J]. Journal of Physics: Conference Series, 2021, 1958 (1): 012015. doi: 10.1088/1742-6596/1958/1/012015
28	ARULAMPALAM M S , MASKELL S , GORDON N , et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Trans.on Signal Processing, 2002, 50 (2): 174- 188. doi: 10.1109/78.978374
29	ZHAO J , LI Z Y . Particle filter based on particle swarm optimization resampling for vision tracking[J]. Expert Systems with Applications, 2010, 37 (12): 8910- 8914. doi: 10.1016/j.eswa.2010.05.086
30	WANG E S , JIA C Y , TONG G , et al. Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm[J]. Advances in Space Research, 2018, 61 (5): 1260- 1272. doi: 10.1016/j.asr.2017.12.016

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Variable structure interactive multiple model filtering and smoothing algorithm

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