Target recognition based on the moment functions of radar signatures

Kyung-Tae Kim; Hyo-Tae Kim

doi:10.1117/12.458742

8 March 2002 Target recognition based on the moment functions of radar signatures

Kyung-Tae Kim, Hyo-Tae Kim

Proceedings Volume 4738, Wavelet and Independent Component Analysis Applications IX; (2002) https://doi.org/10.1117/12.458742
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

In this paper, we present the results of target recognition research based on the moment functions of various radar signatures, such as time-frequency signatures, range profiles, and scattering centers. The proposed approach utilizes geometrical moments or central moments of the obtained radar signatures. In particular, we derived exact and closed form expressions of the geometrical moments of the adaptive Gaussian representation (AGR), which is one of the adaptive joint time-frequency techniques, and also computed the central moments of range profiles and one-dimensional (1-D) scattering centers on a target, which are obtained by various super-resolution techniques. The obtained moment functions are further processed to provide small dimensional and redundancy-free feature vectors, and classified via a neural network approach or a Bayes classifier. The performances of the proposed technique are demonstrated using a simulated radar cross section (RCS) data set, or a measured RCS data set of various scaled aircraft models, obtained at the Pohang University of Science and Technology (POSTECH) compact range facility. Results show that the techniques in this paper can not only provide reliable classification accuracy, but also save computational resources.

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Kyung-Tae Kim and Hyo-Tae Kim "Target recognition based on the moment functions of radar signatures", Proc. SPIE 4738, Wavelet and Independent Component Analysis Applications IX, (8 March 2002); https://doi.org/10.1117/12.458742

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KEYWORDS

Target recognition

Radar

Signal to noise ratio

Scattering

Time-frequency analysis

Advanced distributed simulations

Detection and tracking algorithms

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