Energy efficient target tracking in a sensor network using non-myopic sensor scheduling

We propose to use non-myopic sensor scheduling to minimize the energy usage in a sensor network while maintaining a desired squared-error tracking accuracy of a target's position estimate. The network comprises of Type A sensors that collect measurements, and Type B sensors that collect, process, and schedule measurements. The target is tracked using a particle filter; only Type B sensors hold the target belief and update it with measurements. Network energy consumption is primarily due to sensing and communicating belief and measurements between sensors. To schedule a sequence of M sensing actions, the Type B sensor holding the target belief computes the minimum energy sequence that satisfies the tracking accuracy constraint M steps in the future. Scheduling is implemented efficiently by precomputing an energy tree and using a uniform-cost search. The tracking accuracy for sensor scheduling is approximated by the posterior Cramér-Rao lower bound. Using Monte Carlo simulations, we demonstrate that non-myopic scheduling results in significantly lower energy usage than myopic scheduling while meeting the accuracy constraint.