Multi-sensor Formation Targets Template Matching Tracking Algorithm

DOI

10.2991/mcae-16.2016.34How to use a DOI?

Keywords

template matching; multi-sensor; formation targets; refined tracking

Abstract

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/mcae-16.2016.34
ID  - Wang2016/07
ER  -