Adaptive sequential track-association algorithm based on data quality assessment

被引：0
作者：
Zhang Y. [1 ]
Wu K. [2 ]
Guo J. [1 ]
Ge Z. [2 ]
Zhang B. [1 ]
机构：
[1] School of Aerospace and Engineering, Beijing Institute of Technology, Beijing
[2] Shanghai Electromechanical Engineering Institute, Shanghai
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 11期
关键词：
data quality assessment; entropy method; fuzzy control; multi-source information fusion; track-association;
D O I：
10.12305/j.issn.1001-506X.2022.11.23
中图分类号：
学科分类号：
摘要：
In order to solve the track-association problem when sensor suffers declined accuracy, an adaptive sequential track-association algorithm based on data quality assessment is proposed. Real-time data quality evaluation results are introduced into the adjustment of correlation threshold. The entropy method and the utility function method are combined to evaluate the performance of sensor and the quality of filtering, and the fuzzy control relationship between two indexes and the significance level is constructed, so as to realize the adaptive adjustment of correlation threshold. The simulation results show that the performance of the improved algorithm is better than that of compared algorithm in the situation of declined sensor accuracy, and the good correlation effect is beneficial to the improvement of fusion accuracy. It also has good adaptability in the case of maneuver target tracking. © 2022 Chinese Institute of Electronics. All rights reserved.
引用
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页码：3477 / 3485
页数：8
相关论文
共 31 条
  • [1] BAR-SHALOM Y., Tracking and data association, (1988)
  • [2] NAZARI M, PASHAZADEH S, MOHAMMAD-KHANLI L., An adaptive density-based fuzzy clustering track association for distributed tracking system, IEEE Access, 7, pp. 135972-135981, (2019)
  • [3] SUN K, WEN Y J, CHEN X, Et al., A method for track association using dynamic programming, Proc. of the 6th International Conference on Control, Decision and Information Techno-logies, pp. 1373-1378, (2019)
  • [4] ZHANG X, HUANG H, CHEN Q L, Et al., An improved track association and fusion method, Proc. of the IEEE 14th International Conference on Signal Processing, pp. 854-858, (2018)
  • [5] MA M Y, WANG D J, ZHANG T, Et al., Track-to-track association algorithm for passive multisensor system based on trajectory parameter, IET Radar, Sonar & Navigation, 15, 4, pp. 348-358, (2021)
  • [6] HE Y, ZHANG J W., New track correlation algorithms in a multi-sensor data fusion system, IEEE Trans.on Aerospace and Electronic Systems, 42, 4, pp. 1359-1371, (2006)
  • [7] HE Y, LU D, PENG Y N, Et al., Two new track correlation algorithms in a multisensor data fusion system, Acta Electronica Sinica, 25, 9, pp. 10-14, (1997)
  • [8] TOKTA A, HOCAOGLU A K., A track to track association algorithm based on weighted state correlation similarity, Proc. of the IEEE International Conference on Artificial Intelligence and Data Processing, (2018)
  • [9] KHALEGHI B, KHAMIS A, KARRAY F O, Et al., Multisensor data fusion: a review of the state-of-the-art, Information Fusion, 14, 1, pp. 28-44, (2013)
  • [10] CUI Y Q, LIU Y, TANG T T, Et al., A new adaptive track correlation method for multiple scenarios, IET Radar, Sonar & Navigation, 15, 9, pp. 1112-1124, (2021)
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