Three-dimensional electromagnetic model–based scattering center matching method for synthetic aperture radar automatic target recognition by combining spatial and attributed information

Conghui Ma; Gongjian Wen; Boyuan Ding; Jinrong Zhong; Xiaoliang Yang

doi:10.1117/1.JRS.10.016025

11 March 2016 Three-dimensional electromagnetic model–based scattering center matching method for synthetic aperture radar automatic target recognition by combining spatial and attributed information

Conghui Ma, Gongjian Wen, Boyuan Ding, Jinrong Zhong, Xiaoliang Yang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 1, 016025 (March 2016). https://doi.org/10.1117/1.JRS.10.016025

Abstract

A three-dimensional electromagnetic model (3-D EM-model)–based scattering center matching method is developed for synthetic aperture radar automatic target recognition (ATR). 3-D EM-model provides a concise and physically relevant description of the target’s electromagnetic scattering phenomenon through its scattering centers which makes it an ideal candidate for ATR. In our method, scatters of the 3-D EM-model are projected to the two-dimensional measurement plane to predict scatters’ location and scattering intensity properties. Then the identical information is extracted for scatters in measured data. A two-stage iterative operation is applied to match the model-predicted scatters and the measured data-extracted scatters by combining spatial and attributed information. Based on the two scatter sets’ matching information, a similarity measurement between model and measured data is obtained and recognition conclusion is made. Meanwhile, the target’s configuration is reasoned with 3-D EM-model serving as a reference. In the end, data simulated by electromagnetic computation verified this method’s validity.

Citation Download Citation

Conghui Ma, Gongjian Wen, Boyuan Ding, Jinrong Zhong, and Xiaoliang Yang "Three-dimensional electromagnetic model–based scattering center matching method for synthetic aperture radar automatic target recognition by combining spatial and attributed information," Journal of Applied Remote Sensing 10(1), 016025 (11 March 2016). https://doi.org/10.1117/1.JRS.10.016025

Published: 11 March 2016

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