Hardware Neural Network Implementation of Tracking System

Authors

George G. Lendaris, Portland State UniversityFollow
Robert M. Pap, Accurate Automation Corporation
Richard Saeks, Accurate Automation Corporation
Chas R. Thomas, Accurate Automation Corporation
Richard M. Akita, Accurate Automation Corporation

Published In

Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

Document Type

Citation

Publication Date

12-1-1994

Abstract

A neural network (NN) filter/target-tracking system that accepts and inputs signal data to a noise/target classifier is reported. In its initial design, spectral estimation techniques were used to distinguish noise from real targets. NN was used to calculate the coefficients of an auto regressive linear predictive filter. The current evolution of the design uses Lagrange Multiplier methods to incorporate known character of the noise vs. signal. A (linear) Hopfield NN is used to perform the constrained optimization to solve for the filter coefficients. This algorithm has been demonstrated on real stochastic data.

Locate the Document

https://doi.org/10.1109/NNSP.1994.366025

DOI

10.1109/NNSP.1994.366025

Persistent Identifier

https://archives.pdx.edu/ds/psu/37288

Citation Details

Lendaris, G. G., Pap, R. M., Saeks, R. E., Thomas, C. R., & Akita, R. M. (1994, September). Hardware neural network implementation of tracking system. In Proceedings of IEEE Workshop on Neural Networks for Signal Processing (pp. 451-460). IEEE.