Publications

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 Gaussiannoise. Using Cramer-Rao lower bounds as surrogates for error covariances, we illustrate, using numerical scenarious involving ballistic targets, that the bioinspired algorithm is highly scalable and peforms very well for large numbers of targets.

Through intelligent integration of sensing and processing functions, the sensing technology of the future is evolving towards networks of configurable sensors acting in concert. Realizing the potential of collaborative real-time configurable sensor systems presents a number of challenges including the need to address a number of challenges including the need to address the massive global optimization problem resulting from incorporating a large array of control parameters. This paper proposes a systematic approach to addressing complex global optimization problems by constraining the problem to a set of key control parameters and recasting a mission-oriented goal into a tractable joint optimization formula. Using idealized but realistic physical models, a systematic methodology to approach complex multi-dimensional joint optimization problems is used to compute system performance bounds for dynamic sensor configurations.