Multi-sensor Formation Targets Template Matching Tracking Algorithm
Haipeng Wang, Shuyi Jia, Ziling Wang
Available Online July 2016.
- https://doi.org/10.2991/mcae-16.2016.34How to use a DOI?
- template matching; multi-sensor; formation targets; refined tracking
- Aiming to solve the track refined tracking problem of the formation targets with the multi-sensor detections, based on the relative invariant of the actual positions of the formation targets in each detection period, a new algorithm named multi-sensor formation targets template matching tracking algorithm was proposed. In the algorithm, the template shape matrix and the shape matrix to be matched were respectively obtained with the previous associated formation state set and formation measurement set. The least-cost matching matrix was obtained with the matching search model and the matching matrix validation rules. Moreover, based on the template and the corresponding matching matrix, the state update of each track within the formation targets was completed with the Kalman filter. The analysis results of the simulation data show that obvious advantages of this algorithm are established in the aspects of tracking accuracy, real-time performance and effective tracking rate, compared with the multi-sensor multiplied hypothesis algorithm based on data compressing technical which is a superior performance algorithm in the traditional multi-sensor multi-target tracking field. The real engineering requirement of the refined tracking of the formation targets is met very well with this algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Haipeng Wang AU - Shuyi Jia AU - Ziling Wang PY - 2016/07 DA - 2016/07 TI - Multi-sensor Formation Targets Template Matching Tracking Algorithm BT - Proceedings of the 2016 International Conference on Mechatronics, Control and Automation Engineering PB - Atlantis Press SP - 141 EP - 146 SN - 2352-5401 UR - https://doi.org/10.2991/mcae-16.2016.34 DO - https://doi.org/10.2991/mcae-16.2016.34 ID - Wang2016/07 ER -