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      Multi-feature Combination Track-to-track Association Based on Histogram Statistics Feature

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          Abstract

          Existing track-to-track association methods are mainly based on statistics and fuzzy mathematics.However, most methods based on statistics depend on thresholds, and parameters based on fuzzy mathematics are complex to set. In addition, most methods only consider the information of a single track point in comparison. To solve the existing problems, this paper presents a distance distribution histogram feature to extract the similarity features of a trajectory and measure them using the standardized Euclidean distances; this method effectively utilizes the characteristics of the whole trajectory and has a good anti-noise performance and accuracy. The motion features of ships and the location accuracy of different data sources were fully considered. After obtaining the histogram features of velocity difference and the source features of sensors, the authors combined them and trained association models using machine learning, which effectively avoids the problem of manually setting thresholds and complex parameter settings. Finally, a real ship data set was constructed. The experimental results show that compared with the traditional distance feature, the overall association accuracy was improved by 3.23%~11.65% using the distance distribution histogram feature, and by 0.068% using the combination feature, which verifies the effectiveness of the proposed method.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 February 2019
          : 8
          : 1
          : 25-35
          Affiliations
          [1 ] ①(University of Chinese Academy of Sciences, Beijing 100049, China)
          [2 ] ①(University of Chinese Academy of Sciences, Beijing 100049, China)④(Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China)
          [3 ] ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)
          Article
          fc8cbed76c21486ca980d5c31a54441a
          10.12000/JR18028

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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