Blending Sensor Scheduling Strategy with Particle Filter to Track a Smart Target

We discuss blending sensor scheduling strategies with particle filtering (PF) methods to deal with the problem of tracking a ‘smart’ target, that is, a target being able to be aware it is being tracked and act in a manner that makes the future track more difficult. We concern here how to accurately track the target with a care on concealing the observer to a possible extent. We propose a PF method, which is tailored to mix a sensor scheduling technique, called covariance control, within its framework. A Rao-blackwellised unscented Kalman filter (UKF) is used to produce proposal distributions for the PF method, making it more robust and computationally efficient. We show that the proposed method can balance the tracking filter performance with the observer’s concealment.