Automatic recognition of multiple targets with varying velocities using quadratic correlation filters and Kalman filters
Andrés Rodríguez, Jeffrey Panza, B. V. K. Vijaya Kumar, Abhijit Mahalanobis · 2010
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.