State Fusion Estimation for Asynchronous Multirate Multisensor Systems with Heavy–tailed Noises

This study investigates fusion estimation towards an asynchronous in-multisensor multirate dynamic system with unreliable observations and heavy-tailed noises. Our dynamic system at most detailed level is known. Numerous sensors independently observe a same target, each having distinct sample frequencies, while the observations are unreliable and asynchronously acquired. Those measurement noises and system noise are both non–Gaussian yet heavy–tailed. The sequential fusion estimation approach is formulated. As an efficient extension of traditionary Kalman filter-grounded sequential fusion method with Gaussian noises, both theoretical analysis and experimental evaluations demonstrate the efficacy of our derived approach.