Forward prediction-based approach to target-tracking with Out-of-Sequence Measurements

In target-tracking applications, there may be situations where measurements from a given target arrive out of sequence at the processing center. This problem is commonly referred to as out-of-sequence measurements (OOSMs). So far, most of the existing solutions to this problem are based on retrodiction, where backward prediction of the current state is used to incorporate the OOSMs at the appropriate time. This paper suggests a new method for tackling the OOSMs problem without backward prediction. Based on a forward prediction and de-correlation approach, the method has proved to be as performing as the best retrodiction-based methods, while requiring less data storage in most cases.