Multisensor tracking of a maneuvering target in clutter using IMMPDA fixed-lag smoothing

We present a suboptimal fixed-lag smoothing algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors. The fixed-lag smoothing algorithm is developed by applying the basic interacting multiple model (IMM) approach and the probabilistic data association (PDA) technique to a state-augmented system. In the past this approach had been restricted to Markovian switching systems with no uncertainty regarding the origin of the measurements (i.e., no clutter). The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used and are assumed to operate in a cluttered environment. Compared with an existing IMMPDA filtering algorithm, the proposed smoothing algorithm achieves significant improvement in the accuracy of track estimation by introducing a small time lag between the instants of estimation and latest measurements whereas the computational load increases linearly with lag. However, the tracking delay may lead to undesired effects on control loops in certain applications.