Noise resistant algorithm for radar images recognition and classification

Vesna Zeljković; Qiang Li; Robert Vincelette; Claude Tameze; Fengshan Liu

doi:10.1117/12.828741

1 September 2009 Noise resistant algorithm for radar images recognition and classification

Vesna Zeljković, Qiang Li, Robert Vincelette, Claude Tameze, Fengshan Liu

Proceedings Volume 7445, Signal and Data Processing of Small Targets 2009; 744507 (2009) https://doi.org/10.1117/12.828741
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

We propose a novel algorithm for automatic aircraft classification. The proposed method makes numerical equivalents to shape, size and other aircraft features as critical criteria to constitute the algorithm for their correct classification. This method uses Inverse Synthetic Aperture Radar (ISAR) aircraft images that are making maneuvers that introduce aircraft rotation relative to the radar station. By means of analyzing the shape of the radar pulse and Doppler shifts that are caused by rotation of the aircraft, an image of the aircraft shape can be constructed. We computer simulated five different categories of ISAR images. We tested the proposed classification algorithm on these five categories and on two more categories taken from the Internet. One aircraft model is simulated and the other one is a real sequence with much added noise. All seven different aircraft models are flying a holding pattern. We investigated where in the holding patterns ISAR reflections made it possible to identify each category of aircraft. Our experimental results demonstrate that in most parts of the holding pattern the category of the aircraft can be successfully identified. The performed tests show that the proposed algorithm appears to be noise resistant.

Citation Download Citation

Vesna Zeljković, Qiang Li, Robert Vincelette, Claude Tameze, and Fengshan Liu "Noise resistant algorithm for radar images recognition and classification", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 744507 (1 September 2009); https://doi.org/10.1117/12.828741

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