NEURAL NETWORKS FOR CLASSIFICATION OF RADAR SIGNALS

Authors
  1. Carter, C.A.P.
  2. Masse, N.
Corporate Authors
Defence Research Establishment Ottawa, Ottawa ONT (CAN);Applied Silicon Inc Canada, Ottawa ONT (CAN)
Abstract
Radar Electronic Support Measures (ESM) systems are faced with increasingly dense and commplex electromagnetic environments. Traditional algorithms for signal recognition and analysis are highly complex, computationally intensive, often rely on heuristics, and require humans to verify and validate the analysis. In this paper, the use of an alternative technique - artificial neural networks - to classify pulse-to-pulse signal modulation patterns is investigated. Neural networks are an attractive alternative because of their potential to solve difficult classification attractive alternative because of their potential to solve difficult classification problems more effectively and more quickly than conventionally techniques. Neural networks adapt to a problem by learning, even in a presence of noise or distortion the input data, without the requirement for human programming. In the paper, the fundamentals of network construction, training, behaviour and methods to improve the training process and enhance a network's performance are discussed. As well, a description and the results of the classification experiments are provided.
Keywords
Target identification;Emitter identification;Data bases;Radar pulses
Report Number
DREO-TN-93-33 — Technical Note
Date of publication
01 Nov 1993
Number of Pages
71
DSTKIM No
94-03173
CANDIS No
142878
Format(s):
Hardcopy;Originator's fiche received by DSIS

Permanent link

Document 1 of 1

Date modified: