Feature-aided tracking of ground targets using a class-independent approach

摘要

We have developed and implemented an approach to performing feature-aided tracking (FAT) of ground vehicles using ground moving target indicator (GMTI) radar measurements. The feature information comes in the form of high-range resolution (HRR) profiles when the GMTI radar is operating in the HRR mode. We use a Bayesian approach where we compute a feature association likelihood that is combined with a kinematic association likelihood. The kinematic association likelihood is found using an IMM filter that has onroad, offroad, and stopped motion models. The feature association likelihood is computed by comparing new measurements to a database of measurements that are collected and stored on each object in track. The database consists of features that have been collected prior to the initiation of the track as well as new measurements that were used to update the track. We have implemented and tested our algorithm using the SLAMEM~(TM) simulation.