Identification of radar signals using neural network classifier with low-discrepancy optimisation

A hybrid low-discrepancy sequences optimisation approach is investigated and used for training neural network classifiers for recognition of radar signal emitters. Two sample case studies are developed in order to demonstrate and evaluate the presented approach. In the first one, generic intercepted...

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Bibliographic Details
Main Authors: Petrov, Nedyalko, Jordanov, Ivan, Roe, Jon
Format: Conference Proceeding
Language:English
Subjects:
global optimisation
neural network classification
Optimization
Radar antennas
radar signals identification
Radar tracking
Sociology
Statistics
supervised learning
Training
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Description
Summary:A hybrid low-discrepancy sequences optimisation approach is investigated and used for training neural network classifiers for recognition of radar signal emitters. Two sample case studies are developed in order to demonstrate and evaluate the presented approach. In the first one, generic intercepted radar signals are classified in two broad classes - with civil or military application, based on patterns in their pulse trains, whereas in the second one the classifier is trained to distinguish between several more specific radar functions. Very competitive results of about 84% accuracy are achieved on the testing data sets.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2013.6557890