Adaptive Bayesian tracking with unknown time-varying sensor network performance
- Resource Type
- Conference
- Authors
- Papa, Giuseppe; Braca, Paolo; Horn, Steven; Marano, Stefano; Matta, Vincenzo; Willett, Peter
- Source
- 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. :2534-2538 Apr, 2015
- Subject
- Fields, Waves and Electromagnetics
Artificial neural networks
Lead
Atmospheric measurements
Particle measurements
Clutter
Signal to noise ratio
Multiple sensors
real-world data
Bayesian target tracking
particle filter
time-varying performance
- Language
- ISSN
- 1520-6149
2379-190X
In practical target tracking problems, the target detection performance of the sensors may be unknown and may change rapidly with time. In this work we develop a target tracking procedure able to adapt and react to time-varying changes of the detection capability for a network of sensors. The proposed tracking strategy is based on a Bayesian framework, in which the dynamic target state is augmented to include the sensor detection probabilities. The method is validated using computer simulations and real-world experiments conducted by the NATO Science and Technology Organization (STO) - Centre for Maritime Research and Experimentation (CMRE).