Track state augmentation for estimation of probability of detection in multistatic sonar data

This paper presents results of augmenting the track state with an amplitude offset to predict the probability of detection for a target moving through a multistatic field. The amplitude offset in the state allows for the local modeling of the environment, accounting for environmental modeling errors, and differentiating between target types. The approach is evaluated on the PACsim multistatic sonar dataset, a simulated dataset created for tracker evaluation by the Multistatic Tracking Working Group. Tracking and data association are done using Monte Carlo Joint Probabilistic Data Association, which is a particle-filter based implementation of JPDA. Results on the simulated data suggest that improved modeling must be done for this approach to be viable.