Multitarget-tracking optical processing system

We consider multitarget tracking, estimating the state vector (two-dimensional position and velocity) of each target from physical measurements. We consider a full system for this and the role for analog optical processing within its subsystems. We emphasize the neural network data-association subsystem (which associates measurements in the present input frame with estimates from previous frames of data). Our new optimization neural net results concern associations between measurements and estimates and show that use of a simple fixed-coefficient estimation filter is sufficient. For completeness in our full system approach we briefly describe our optical detection subsystem and its use to reduce frame-to-frame jitter in the measurements. We also briefly note our Hough-transform optical subsystem and discuss its use in detecting and correcting data dropout errors and errors in the data-association and estimator systems. We conclude that analog optical processing has significant use in a full multitarget tracking system.