Research on Multi-sensor Cooperative Tracking Mission Planning of Aerospace Hypersonic Vehicles

DOI

10.2991/caai-17.2017.44How to use a DOI?

Keywords

Aerospace hypersonic vehicles; multi-sensors; cooperative tracking; mission planning; self-adapting clonal genetic algorithms

Abstract

Aimed at aerospace hypersonic vehicles (AHV) with the characteristics of high velocity, maneuverability, Radar Cross-section (RCS) weak, the single sensor is difficult to effectively track, therefore proposed multi-sensor collaborative workflow, construct cooperative tracking mission planning framework based on multi-agent system (MAS), and then multi-sensor cooperative optimization model is established. Proposed collaborative tracking mission planning algorithm based on Self-adaptive clonal genetic algorithm (SCGA). Simulation results validate the model, algorithm to establish is rationality and superiority.

Copyright

© 2017, 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/).

Download article (PDF)

TY  - CONF
AU  - Qiang Fu
AU  - Chengli Fan
AU  - Gang Wang
AU  - Xiangke Guo
PY  - 2017/06
DA  - 2017/06
TI  - Research on Multi-sensor Cooperative Tracking Mission Planning of Aerospace Hypersonic Vehicles
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 201
EP  - 206
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.44
DO  - 10.2991/caai-17.2017.44
ID  - Fu2017/06
ER  -