ON OPTIMAL TRACK-TO-TRACK FUSION

Track-to-track fusion is an important part in multisensor fusion. Much research has been done in this area, Chong et al. [5-7] among others, presented an optimal fusion formula under an arbitrary communication pattern, This formula is optimal when the underlying systems are deter ministic, i.e., the process noise Is zero, or when full-rate communica tion (two sensors exchange information each time they receive new meas urements) Is employed. However, in practice, the process noise is not negligible due to target maneuvering and sensors typically communicate infrequently to save communication bandwidth, In such situations, the measurements from two sensors are not conditionally (given the previo us target state) independent due to the common process noise from the underlying system, and the fusion formula [7] becomes an approximate o ne, This dependence phenomena was also observed by [1] where a formula was derived to compute the cross-covariance of two track estimates ob tained by different sensors, Based on the results in [1], a fusion for mula was subsequently derived [2] to combine the local estimates which took into account the dependency between the two estimates, Unfortuna tely, the Bayesian derivation in [2] made an assumption that is not me t. This work points out the implicit approximation made in [2] and sho ws that the result turns out to be optimal only in the ML (maximum lik elihood) sense, A performance evaluation technique is then proposed to study the performance of various track-to-track fusion techniques. Th e results provide performance bounds of different techniques under var ious operating conditions which can be used in designing a fusion syst em.