Home Journals Articles Updates About
CN
image
收藏切换
Vehicle Target Tracking Based on Improved Adaptive Interacting Multiple Model-unscented Kalman Filter Algorithm
收藏切换
PDF
Ben-yang NAN, Bing KUANG*, Hui JING
Science Technology and Engineering | 2025, 25(11) : 4605 - 4611
Less
收藏切换
Science Technology and Engineering | 2025, 25(11): 4605-4611
• Papers·Electronic and Communicational Technology •
Vehicle Target Tracking Based on Improved Adaptive Interacting Multiple Model-unscented Kalman Filter Algorithm
Full
Ben-yang NAN, Bing KUANG*, Hui JING
Affiliations
  • School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
Published: 2025-04-18 doi: 10.12404/j.issn.1671-1815.2309042
Outline
收藏切换

In order to solve the problems of the traditional interactive multiple model (IMM) algorithm in vehicle target tracking, such as the model probability change is not obvious and the tracking accuracy is insufficient, an improved adaptive IMM-UKF(unscented Kalman filter) algorithm was proposed. Firstly, the vehicle motion model was established by using uniform speed straight line, uniform acceleration straight line and uniform turning, and the vehicle target was tracked by unscented Kalman filter. Then, the probability change rate of sub model was used as the correction parameter of IMM algorithm, and different correction strategies were adopted for the main diagonal and non main diagonal elements of Markov matrix. Finally, the decision window was set to modify the main diagonal element of the normalized Markov matrix to expand the probability of matching model. The results show that the probability of the improved algorithm model changes more obviously, and the root mean square errors of position and velocity are less than the original algorithm, which effectively improves the tracking accuracy.

target tracking  /  interacting multiple model  /  adaptive  /  Markov matrix  /  unscented Kalman filter
Ben-yang NAN, Bing KUANG, Hui JING. Vehicle Target Tracking Based on Improved Adaptive Interacting Multiple Model-unscented Kalman Filter Algorithm[J]. Science Technology and Engineering, 2025 , 25 (11) : 4605 -4611 . DOI: 10.12404/j.issn.1671-1815.2309042
Appendix
Less
Year 2025 volume 25 Issue 11
PDF
433
180
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2309042
  • Receive Date：2023-11-17
  • Online Date：2025-07-09
  • Published：2025-04-18
Article Data
Affiliations
History
  • Received：2023-11-17
  • Revised：2024-08-10
Funding
Affiliations
    School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2309042
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House