Multisensor tracking of a maneuvering target in clutter with asynchronous measurements using IMMPDA filtering and parallel detection fusion

We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors dealing with possibly asynchronous (time delayed) measurements. The filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach, the probabilistic data association (PDA) technique, and asynchronous measurement updating to a state-augmented system for the target motion. A state augmentation approach is developed to estimate the time delay between local and remote sensors. A multisensor probabilistic data association filter is developed for parallel sensor processing for target tracking under clutter. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, radar and an infrared sensor, are used. Compared with an existing IMMPDA filtering algorithm designed for synchronous (no delay) sensor measurements processing, the proposed algorithm achieves considerable improvement in the accuracy of track estimation.