Title :
Wavelet feature extractors and evidence neural networks for the detection of “blackholes” in backscatter ionograms
Author :
Skafidas, E. ; Palaniswami, M. ; Percival, D.J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
fDate :
29 Nov-2 Dec 1994
Abstract :
Backscatter ionograms provide a concise summary of high frequency (HF) ionospheric propagation conditions, and find application in over-the-horizon radar (OTHR) and HF communications. Of particular interest are blackholes in backscatter ionograms, which are manifest as anomalous linear depressions in the ionogram clutter. An algorithm is developed for the real time automatic detection of blackholes. The algorithm uses a wavelet based feature extractor derived for a simple blackhole model, which in turn is presented as input to a multistage evidence based neural network classifier. Classification results are favourably compared with classical template matching
Keywords :
backscatter; feature extraction; ionospheric electromagnetic wave propagation; ionospheric techniques; neural nets; pattern classification; radar computing; radiowave propagation; wavelet transforms; HF communications; anomalous linear depressions; backscatter ionograms; blackhole detection; classical template matching; classification results; evidence neural networks; high frequency ionospheric propagation conditions; ionogram clutter; multistage evidence based neural network classifier; over-the-horizon radar; real time automatic detection; simple blackhole model; wavelet based feature extractor; wavelet feature extractors; Backscatter; Clutter; Feature extraction; Frequency; Intelligent networks; Neural networks; Radar detection; Signal resolution; Signal to noise ratio; Wavelet domain;
Conference_Titel :
Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-7803-2404-8
DOI :
10.1109/ANZIIS.1994.396953
