Automatic backtracking-based refined segment recognition of maneuvering target track

被引：0
|
作者：
Qiao D. [1 ]
Liang Y. [1 ]
Zhang H. [1 ]
Zhao P. [2 ]
机构：
[1] Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an
[2] North Automatic Control Technology Institute, Taiyuan
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2021年 / 42卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Backtracking iterated optimization; Maneuvering targets; Pattern switching; Track segmentation; Two-layer flight track segmentation;
D O I：
10.7527/S1000-6893.2020.24744
中图分类号：
学科分类号：
摘要：
Segment recognition of the maneuvering target track is the basis for judging the intention of target's behavior. However, existing track segmentation algorithms have a weak ability to detect changes of pattern, and are thus difficult to meet the requirement of fast and refined track segmentation for maneuvering targets. To this end, our paper proposes a two-layer refined track segmentation framework. The pre-segmentation layer is used to detect the pattern switching during the movement of the target, so as to determine the pre-segment area with obvious pattern changes and obtain the pre-segment points of the area with obvious target pattern changes. Then, iterative backtracking optimization is used to segment the track of the non-pre-segmented area with small differences, so as to obtain more refined segmentation points. The framework has the ability to process the track from coarse to fine segmentation, and can realize the recognition of refined segment of the maneuvering target track. Finally, the simulation results of two typical target maneuvering scenarios are given to demonstrate the effectiveness of our proposed method, which can not only reduce the time of iterative optimization, but also improve segmentation accuracy. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.
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共 22 条
  • [1] WANG M M, ZHANG S G., Adaptive trajectory tracking algorithm based on tracking model predictive static programming, Acta Aeronautica et Astronautica Sinica, 39, 9, (2018)
  • [2] ZHANG D Z, LEE K, LEE I., Hierarchical trajectory clustering for spatio-temporal periodic pattern mining, Expert Systems with Application, 92, pp. 1-11, (2018)
  • [3] GAO C, ZHANG Z, HUANG C, Et al., Semantic trajectory representation and retrieval via hierarchical embedding, Information Sciences, 538, pp. 176-192, (2020)
  • [4] ZHANG D Z, LEE K, LEE I., Semantic periodic pattern mining from Spatio-temporal trajectories, Information Sciences, 502, pp. 164-189, (2019)
  • [5] GENG Z Q, CHEN G F, HAN Y M, Et al., Semantic relation extraction using sequential and tree-structured LSTM with attention, Information Sciences, 509, pp. 183-192, (2019)
  • [6] ZHANG D, LEE K, LEE I., Mining hierarchical semantic periodic patterns from GPS-collected Spatio-temporal trajectories, Expert Systems with Applications, 122, pp. 85-101, (2018)
  • [7] LIU L, WANG S, SU G, Et al., A framework of mining semantic-based probabilistic event relations for complex activity recognition, Information Sciences, 418, pp. 13-33, (2017)
  • [8] SHEU H T, HU W C., Multiprimitive segmentation of planar curves-A two-level breakpoint classification and tuning approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, pp. 791-797, (1999)
  • [9] PITTMAN J, MURTHY C A., Fitting optimal piecewise linear functions using genetic algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, pp. 701-718, (2000)
  • [10] LINDER S P, SCHELL C., A non-Bayesian segmenting tracker for highly maneuvering targets, IEEE Transactions on Aerospace & Electronic Systems, 41, 4, pp. 1168-1177, (2005)
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