Finite-precision discrete-time neural network data association
Oluseyi Olurotimi, Clayton V. Stewart, Roger Novack · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
This paper describes a discrete-time analog neural network solution to the data association, or data correlation problem. This work, which is an extension of previous investigations, was originally motivated by the earlier results of Sengupta and Iltis (1989). In this paper, we exploit the fact that the associated optimization problem is loosely described, and map the data association problem onto an analog discrete-time neural network connected in an on-center, off-surround configuration. This reduces the number of parameters required in the system design, thereby also reducing the system sensitivity to parameter variations, and leading to greater robustness. Results are presented for simulations performed on a typical workstation. Simulations were also performed with reduced precision numbers. The performance in both cases were not identical, and parameter adjustments in a specific direction are needed in the finite-precision case for acceptable performance.