A nonlinear estimation algorithm and its optical implementation for target tracking in clutter environment

Abstract

The two-dimensional tracking problem in a clutter environment is solved in the-discrete-time Bayes optimal (nonlinear, and non-Gaussian) estimation framework. The proposed method recursively finds the probability density functions of the target position and velocity. With our approach, the nonlinear estimation problem is converted into simpler linear convolution operations that can be implemented with optical devices efficiently. We present a possible optical implementation architecture, and its functionality is, verified through computer simulation.