<title>Nonlinear estimation algorithm and its optical implementation for target tracking in clutter environment</title>

Abstract

The systems such as infrared search and trackers (IRST's), forward looking infrared systems (FLIR's), sonars, and 2-D radars consist of two functional blocks; a detection unit and a tracker. The detection unit which has matched filters followed by a threshold device generates a set of multiple two-dimensional points or detects at every sampling time. For a radar or sonar, each generated detect has polar coordinates, the range and azimuth while an IRST or FLIR produces detects in cartesian coordinates. In practice, the detection unit always has a non-zero false alarm rate, and therefore, the set of detects usually contains clutter points as well as the target. In this paper, we shall present a new target tracking algorithm for clutter environment applicable to a wide range of tracking systems. More specifically, the two-dimensional tracking problem in clutter environment is solved in the discrete-time Bayes optimal (nonlinear, and non-Gaussian) estimation framework. The proposed method recursively finds the entire probability density functions of the target position and velocity. With our approach, the nonlinear estimation problem is converted into simpler linear convolution operations, which can efficiently be implemented with optical devices such as lenses, CCD's (charge coupled devices), SLM's (spatial light modulators) and films.