Automatic recognition of multiple targets with varying velocities using quadratic correlation filters and Kalman filters

Automatic target recognition (ATR) systems require detection, recognition, and tracking algorithms. The classical approach is to treat these three stages separately. In this paper, we investigate a correlation filter (CF)-based approach that combines these tasks for enhanced ATR. We present a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way. Our contribution is a framework that is able to locate multiple targets with different velocities at unknown positions providing enhanced ATR with only a marginal increase in computation over other CF ATR algorithms.