A Sensor Scheduling Model of Midcourse Object Tracking Based Signal-to-noise Ratio Optimization

DOI

10.2991/wartia-16.2016.279How to use a DOI?

Keywords

Signal-to-noise optimization, Multi-sensor management, Object continue tracking,

Abstract

This paper proposes a sensor dynamic scheduling algorithm of midcourse target tracking. This algorithm increases SNR constraints to optimization function which treats Geometric Dilution of Precision (GDOP) as the scheduling criterion. Firstly, by analyzing the different process technologies of midcourse target tracking handoff, this paper proposes a dual-stage of technical idea including recapture confirmation and stable tracking. Secondly, at the recapture confirmation stage, for the purpose of increasing the target recapture probability, this paper verifies that it’s necessary and reasonable to increase the target signal to noise ratio as a key constraint in considering the basic factors of the camera detection performance ,observed background, stray light and so on. In stable tracking phase, ,this paper adopts a simplified geometric model which can get higher tracking accuracy to schedule the sensor. Next, The performance of the proposed model are analyzed by using the real particle swarm optimization algorithm that reduces the dimension of the particles and updates the position vector. The simulation results show that the model is more effective.

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  - Yuanyuan Yang
AU  - WeiDong Sheng
AU  - Wei An
PY  - 2016/05
DA  - 2016/05
TI  - A Sensor Scheduling Model of Midcourse Object Tracking Based Signal-to-noise Ratio Optimization
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1344
EP  - 1351
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.279
DO  - 10.2991/wartia-16.2016.279
ID  - Yang2016/05
ER  -