Volume 8 Issue 1
Mar.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(1): 25-35
XU Yasheng, DING Chibiao, REN Wenjuan, et al. Multi-feature combination track-to-track association based on histogram statistics feature[J]. Journal of Radars, 2019, 8(1): 25–35. doi: 10.12000/JR18028
Citation: XU Yasheng, DING Chibiao, REN Wenjuan, et al. Multi-feature combination track-to-track association based on histogram statistics feature[J]. Journal of Radars, 2019, 8(1): 25–35. doi: 10.12000/JR18028
shu

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

doi: 10.12000/JR18028
  • ①.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • ②.

    Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China

  • ③.

    Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China

  • ④.

    Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China

Funds:  The National Natural Science Foundation of China (61725105, 61331017)
More Information
  • Corresponding author: XU Yasheng, xuyasheng93@163.com
  • Received Date: 2018-03-29
  • Rev Recd Date: 2018-05-29
    • Available Online: 2018-07-09
  • Publish Date: 2019-02-28
    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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