Multiresolution approach to object classification using kinematic features

Hai-Wen Chen; Harry A. Schmitt; Jack G. Riddle; Stephanie K. Mashima; Dennis M. Healy Jr.

doi:10.1117/12.342948

22 March 1999 Multiresolution approach to object classification using kinematic features

Hai-Wen Chen, Harry A. Schmitt, Jack G. Riddle, Stephanie K. Mashima, Dennis M. Healy Jr.

Author Affiliations +

Proceedings Volume 3723, Wavelet Applications VI; (1999) https://doi.org/10.1117/12.342948
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

A multi-resolution approach to problems of the identification of classes of ballistic missile objects is outlined. This approach is based on the utilization of features estimated from time-varying infrared signatures and the subsequent discrimination of different objects using unique time-frequency patterns obtained from a multi- resolution decomposition of the training and observation (performance evaluation) data. For example, we have identified four features that show some promise for discrimination: the intensity in the second lowest sub-band, the temporal profile in the lowest frequency sub-band, the modulation intensity, and the DC level of each observed object. The multi-resolution discrimination algorithm's performance can be evaluated by comparing with more traditional Fourier based approaches. The multi-resolution discrimination algorithms were applied to simulated data and were shown, by using L₁ or L₂ norms as distance metrics, to provide good classification performance and to reduce the temporal data length by half. The features extracted using the discrete wavelet packet transform can help to further improve classification performance. The robustness of the algorithm in the presence of noise is also studied. All data sets were generated with Raytheon Missile Systems Company's high fidelity simulation.

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Hai-Wen Chen, Harry A. Schmitt, Jack G. Riddle, Stephanie K. Mashima, and Dennis M. Healy Jr. "Multiresolution approach to object classification using kinematic features", Proc. SPIE 3723, Wavelet Applications VI, (22 March 1999); https://doi.org/10.1117/12.342948

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