Share Email Print

Proceedings Paper

Space object identification using spatiotemporal pattern recognition
Author(s): Gary Brandstrom; Dennis W. Ruck; Steven K. Rogers; Bruce E. Stribling
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper demonstrates the application of new pattern recognition techniques that can be used to characterize space objects. The feature space trajectory neural network (FST NN) was first presented by Leonard Neiberg and David P. Casasent in 1994 as a target identification tool. Kenneth H. Fielding and Dennis W. Ruck recently applied the hidden Markov model (HMM) classifier to a 3D moving light display identification problem and a target recognition problem, using time history information to improve classification results. This paper shows how the FST NN and HMM can be used to automate optical detection of space object anomalies. Time sequenced images produced by a simulation program are used for testing these anomaly detection algorithms. Two data sets are tested. The second data set is tested with various levels of shot noise. The first data set is more difficult to classify, with the best FST NN test achieving a 100% anomaly detection rate with 5% false alarm rate. FST NN and HMM tests on the second data set achieved 100% anomaly detection with no false alarms. With various levels of shot noise added, the FST NN achieves a 100% anomaly detection rate with 4% false alarm rate. A variety of Fourier features are tested with energy normalized low frequency coefficients producing the best consistent results across data sets and noise levels. A new FST NN test is presented that measures how well the order of a test sequence matches other sequences in the database. The original FST NN is based strictly on feature space distance, but when the order of the sequence is important, the new test is useful.

Paper Details

Date Published: 22 March 1996
PDF: 12 pages
Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); doi: 10.1117/12.235937
Show Author Affiliations
Gary Brandstrom, Air Force Institute of Technology (United States)
Dennis W. Ruck, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Bruce E. Stribling, Air Force Maui Optical Station (United States)

Published in SPIE Proceedings Vol. 2760:
Applications and Science of Artificial Neural Networks II
Steven K. Rogers; Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?