Tracking Fusion with Dissimilar Sensors for Linear Systems with Correlated Measurement Noises

Tracking fusion with dissimilar sensors, which is a challenge work in multisensor fusion, is studied. Under the condition of general correlated measurement noises, the centralized and distributed tracking fusion question is investigated based on linear unbiased minimum variance estimation theory. The basic algorithms of measurement fusion and state vector fusion are presented in linear system with dissimilar sensors by the way of sequential filtering. These algorithms involve with not only the correlated measurement noises but also the configuration difference in local sensors, so information about multi-sensors fusion is increased. Through a simulation example it is indicated that the results of proposed algorithm is better than that classic ones where the measurement noises and processing noises are assumed to be uncorrelated.