00000cab a22000003a 4500 UP-99796217609467395 Buklod 20231007234323.0 m |o d | ta 100918s xx d | ||r ||||| || DENGII eng Kyung-Tae Kim Efficient radar target classification using adaptive joint time-frequency processing. pp. 1789-1801 This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition Adaptive Gaussian representation. Adaptive joint time-frequency processing. Adaptivity spectrogram. Closed form expressions. Efficient radar target classification. Exact expressions. Feature extraction. Feature space dimension reduction. Feature vectors. Frequency domain. Geometrical moments. Joint time-frequency domain. Multilayer perceptron neural network. Performance. Principal component analysis. Target recognition. Thin-wire targets. Time-domain. IEEE Transactions on antennas and propagation 48, 12 (2000). FO UPD DENG-II Article