Practical distributed maneuvering target tracking using delayed information of heterogeneous unregistered sensors

Abstract

Registration is the most consequential topic to be dealt with in a multi-sensor tracking system. On the other hand, providing satisfactory target acceleration estimation would enhance the performance of a ground-based air defense system encountering a maneuvering target. The main novelty of the present work is addressing a new scheme to solve the registration problem in a distributed network along with estimating accurate target acceleration, simultaneously. The details of coping with three common kinds of measurement, attitude, and location biases are explored concentrating on the effects of attitude bias as the main error source from the practical viewpoint. A modified iterated extended Kalman filter is suggested as the basis to handle the multi-rate fusion problem. The probable performance degradation due to the existence of sensors measurement delay (ignored in all similar studies) is illustrated. An effective scheme that compensates for the delay with a low computation cost is introduced. The formulation is developed for a typical radar-infrared tracking system, accomplished by elucidating the required points for generalization to a desirable number of heterogeneous sensors. Calculating the Cramer-Rao bound of estimation problem, the effectiveness of proposed scheme is deliberately demonstrated through simulation analyses respecting the momentous practical aspects.