NDLI: Radar signal categorization using a neural network

Content Provider	IEEE Xplore Digital Library
Author	Anderson, J.A. Gately, M.T. Penz, P.A. Collins, D.R.
Copyright Year	1963
Abstract	Neural networks are used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy-minimizing network. The limiting process contains the state vector within a set of limits, and this model is called the brain state in a box, or BSB model, which forms energy minima (attractors in the network dynamical system) based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to tentatively identify them based on their observed parameters. As a test of this idea, a neural network was used to form a small database that could potentially make emitter identification. There were three errors of classification. The number of iterations are required to reach an attractor state was very long, and some of the final states were not fully limited. These factors indicate the uncertainty of the neural network.<>
Sponsorship	IEEE Publication
Starting Page	1646
Ending Page	1657
Page Count	12
File Size	1156164
File Format	PDF
ISSN	00189219
Volume Number	78
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radar Neural networks Biological neural networks Brain modeling Analytical models Working environment noise Noise generators Pulse generation Testing Databases
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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