Bioinspired resource management for multiple-sensor target tracking systems

We present an algorithm, inspired by self-organization and stigmergy observed in biological swarms, for managing multiple sensors tracking large numbers of targets. We devise a decentralized architecture wherein autonomous sensors manage their own data collection resources and task themselves. Sensors cannot communicate with each other directly; however, a global track file, which is continuously broadcast, allows the sensors to infer their contributions to the global estimation of target states. Sensors can transmit their data (either as raw measurements or some compressed format) only to a central processor where their data are combined to update the global track file. We outline information-theoretic rules for the general multiple-sensor Bayesian target tracking problem. We provide specific formulas for problems dominated by additive white Gaussian noise. Using Cramér-Rao lower bounds as surrogates for error covariances, we illustrate, using numerical scenarios involving ballistic targets, that the bioinspired algorithm is highly scalable and performs very well for large numbers of targets.