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

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

[1]  Alfred O. Hero,et al.  Sensor management and provisioning for multiple target radar tracking systems , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Yang Yi,et al.  Complex terrain classification algorithm based on multi-sensors fusion , 2013, Proceedings of the 32nd Chinese Control Conference.

[3]  Erik Frisk,et al.  EKF-based adaptation of look-up tables with an air mass-flow sensor application , 2011 .

[4]  Lisa Ann Osadciw,et al.  A controllable sensor management algorithm capable of learning , 2005, SPIE Defense + Commercial Sensing.

[5]  Hani Pourvaziri,et al.  A hybrid multi-population genetic algorithm for the dynamic facility layout problem , 2014, Appl. Soft Comput..

[6]  Albert P. M. Wagelmans,et al.  Online stochastic UAV mission planning with time windows and time-sensitive targets , 2014, Eur. J. Oper. Res..

[7]  M. Mirshams,et al.  A Bayesian networks approach to reliability analysis of a space vehicle separation sub-system , 2013, 2013 6th International Conference on Recent Advances in Space Technologies (RAST).

[8]  William Moran,et al.  Sensor management via riemannian geometry , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[10]  Baoping Tang,et al.  A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm , 2013 .

[11]  Wen-Qin Wang,et al.  Near-Space Vehicle-Borne SAR With Reflector Antenna for High-Resolution and Wide-Swath Remote Sensing , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Yang Shao-chun.  Task Programming of Antiballistic Missile Sensors Based on MAS , 2012 .

[13]  Mou Chen,et al.  Composite nonlinear control for near space vehicles with input saturation based on disturbance observer , 2013, Proceedings of the 32nd Chinese Control Conference.

[14]  Elizabeth S. Bentley,et al.  Effective Resource Management in Visual Sensor Networks With MPSK , 2013, IEEE Signal Processing Letters.

[15]  Daniel E. Koditschek,et al.  Toward dynamical sensor management for reactive wall-following , 2013, 2013 IEEE International Conference on Robotics and Automation.

[16]  E. Markin.  Scatter pattern calculations and determination performance limitations of existing air defense systems for intercepting hypersonic vehicles , 2012, 2012 IEEE Radar Conference.

[17]  Zeng Hui.  Sensor scheduling for missile early-warning satellite based on genetic and simulated annealing algorithm , 2006 .

[18]  Jonas Nygards,et al.  A sensor management framework for autonomous UAV surveillance , 2005, SPIE Defense + Commercial Sensing.

[19]  Simon X. Yang,et al.  A Neural Network Approach to Dynamic Task Assignment of Multirobots , 2006, IEEE Transactions on Neural Networks.

[20]  Changyin Sun,et al.  Finite time integral sliding mode control of hypersonic vehicles , 2013 .