Multiple sensor fusion using adaptive Divided Difference information filter

This paper addresses the problem of multiple sensor fusion in situations where the system dynamics suffers from unknown parameter variation. An adaptive nonlinear information filter has been proposed for such multi sensor estimation problems where the process noise covariance becomes unknown as a consequence of unknown parameter variation. The proposed filter, based on the Divided Difference interpolation formula, ensures satisfactory estimation performance by online adaptation of the unknown process noise covariance and makes sensor fusion successful. Efficacy of the proposed filter is demonstrated with the help of a tracking problem in a sensor fusion configuration. Results from Monte Carlo simulation indicate that though the process noise covariance is unknown, the performance of the proposed filter is demonstrably superior to its non adaptive version in the context of joint estimation of parameter and states.