The use of particle filtering with the unscented transform to schedule sensors multiple steps ahead

In a multisensor network, sensor scheduling can be used to minimize the cost of resources and improve system performance. We propose a multisensor scheduling algorithm using a particle filter and the unscented transform for a target tracking application. Under the constraint that only one sensor may be used at each time step, we predict the expected cost multiple steps ahead. We achieve this using several sets of particles for each sequence of sensors and then choose the sequence that minimizes the predicted cost. An advantage of the proposed algorithm is that it can incorporate arbitrary cost functions. Monte Carlo simulations, using squared error as the cost function, demonstrate the improved target tracking performance achieved with sensor scheduling.