Radar HRRP statistical recognition based on hypersphere model
Signal Processing
Integrated Computer-Aided Engineering
Intelligent target recognition based on wavelet packet neural network
Expert Systems with Applications: An International Journal
Intelligence techniques for prostate ultrasound image analysis
International Journal of Hybrid Intelligent Systems
An evolution of geometric structures algorithm for the automatic classification of HRR radar targets
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Intelligent target recognition based on wavelet adaptive network based fuzzy inference system
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Radar HRRP recognition based on discriminant information analysis
WSEAS Transactions on Information Science and Applications
Radar target recognition based on fuzzy optimal transformation using high-resolution range profile
Pattern Recognition Letters
A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)
Expert Systems with Applications: An International Journal
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This paper explores the use of wavelets to improve the selection of discriminant features in the target recognition problem using high range resolution (HRR) radar signals in an air to air scenario. We show that there is statistically no difference among four different wavelet families in extracting discriminatory features. Since similar results can be obtained from any of the four wavelet families and wavelets within the families, the simplest wavelet (Haar) should be used. We use the box classifier to select the 128 most salient pseudo range bins and then apply the wavelet transform to this reduced set of bins. We show that by iteratively applying this approach, the classifier performance is improved. We call this the iterated wavelet transform . The number of times the feature reduction and transformation can be performed while producing improved classifier performance is small and the transformed features are shown to quickly cause the performance to approach an asymptote.