Out-of-sequence processing of cluttered sensor data using multiple evolution models

In target tracking applications, the full information on the kinematic target states accumulated over a certain time window up to the present time is contained in the joint probability density function of these state vectors, given the time series of all sensor data. In [1] the structure of this Accumulated State Density (ASD) has been revealed. Furthermore, ASDs enable us to process Out-of-Sequence (OoS) measurements in a neat and straightforward way. This paper presents an algorithm for the processing of OoS measurements in situations with more relaxed assumptions. On the one hand, sensors often return ambiguous measurement data. Then, measurement association methodologies as the Multi-Hypothesis Tracker (MHT) are required. On the other hand, the evolution model in use might not be unique. The well-known approach to this challenge is the Interacting Multiple Model (IMM) filter. In this paper, an IMM/MHT extension to the ASD paradigm is discussed, tested by simulation, and evaluated.