Vehicle Target Tracking Based on Kalman Filtering Improved Compressed Sensing Algorithm

被引：0
|
作者：
Zhou Y. [1 ,2 ]
Hu J. [1 ]
Zhao Y. [1 ]
Zhu Z. [1 ,3 ]
Hao G. [1 ]
机构：
[1] College of Civil Engineering, Hunan University, Changsha
[2] Hunan Provincial Key Laboratory for Damage Diagnosis of Engineering Structures（Hunan University）, Changsha
[3] Changsha Construction Project Quality and Safety Supervision Station, Changsha
来源：
Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2023年 / 50卷 / 01期
基金：
中国国家自然科学基金;
关键词：
compressed sensing; Kalman filter; naive Bayes classifiers; target detection; target tracking;
D O I：
10.16339/j.cnki.hdxbzkb.2023002
中图分类号：
学科分类号：
摘要：
Aiming at the tracking-shift of traditional target tracking algorithms based on compressed sensing technology，a vehicle target tracking algorithm based on Kalman filtering improved compressed sensing algorithm was proposed in this paper. Firstly，the observed value was obtained by identifying the region with the highest prob⁃ ability of target existence in this frame based on the traditional compressed sensing target tracking algorithm. Sec⁃ ondly，the Kalman filter was used to predict the tracking trajectory of this frame so as to obtain the predicted value，and the Kalman filter gain coefficient was used to correct the predicted value and the observed value to obtain the fi⁃ nal target tracking result. Finally，positive and negative samples were taken around the corrected target area to real⁃ ize the updating of naive Bayes classifiers，and then the real-time updating of target tracking trajectory was achieved. The feasibility of the proposed method was verified by laboratory tests and field experiments. The average tracking error of the proposed method is reduced by 48% and 89%，respectively，compared with the target tracking algorithm based on compressed sensing technology. The tracking trajectory was closer to the real vehicle trajectory. © 2023 Hunan University. All rights reserved.
引用
收藏
页码：11 / 21
页数：10
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