SCHEDULINGMULTIPLE SENSORS USING PARTICLE FILTERS IN TARGET TRACKING

A critical component of a multi-sensor system is sensor sche-duling to optimize system performance under constraints (e.g. power, bandwidth, and computation). In this paper, we apply particle filter sequential Monte Carlo methods to im-plement multiple sensor scheduling for target tracking. Un-der the constraint that only one sensor can be used at each time step, we select a sequence of sensor uses to minimize the predicted mean-square error in the target state estimate; the predicted mean-square error is approximated using the particle filter in conjunction with an extended Kalman filter approximation. Using Monte Carlo simulations, we demon-strate the improved performance of our scheduling approach over the non-scheduling case