Multitarget detection using Kalman-based average displacement estimator

Abstract

The anomaly in the displacement estimates obtained from a low-level Kalman-based average displacement estimator is used to detect multiple targets (obtain returns) in a forward-looking infrared (FLIR) scene. In future, the displacement estimates originating from the targets could be associated with the returns (detected multiple targets of this work), and hence obtain an updated estimated position of the targel. This procedure could be repeated for all targets in the scene to obtain multitarget tracking. The behavior of the expected value of the displacement estimates as a function of the number of iterations is investigated. Also, the behavior of the displacement error covariance matrix and the Kalman gain matrix are investigated as a function of the number of iterations.