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PIER PIER B PIER C PIER M PIER Letters
2014-01-29
Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar
By
Seung-Jae Lee,

    Dr. Seung-Jae Lee

    Department of Electronic Engineering

    Hannam University

    Korea

    Homepage

In-Sik Choi,

    Prof. In-Sik Choi

    Electronic Engineering

    Hannam University

    Korea

    Homepage

Byunglae Cho,

    Dr. Byunglae Cho

    Agency for Defense Development

    South Korea

    Homepage

Edward J. Rothwell,

    Professor Edward J. Rothwell

    Department of Electrical and Computer Engineering

    Michigan State University

    USA

    Homepage

Andrew K. Temme

    Dr. Andrew K. Temme

    Department of Electrical and Computer Engineering

    Michigan State University

    USA

    Homepage

Progress In Electromagnetics Research, Vol. 144, 291-302, 2014
doi:10.2528/PIER13103101
Abstract
This paper proposes a fusion technique of feature vectors that improves the performance of radar target recognition. The proposed method utilizes more information than simple monostatic or bistatic (single receiver) algorithms by combining extracted feature vectors from multiple (two or three) receivers. In order to verify the performance of the proposed method, we use the calculated monostatic and bistatic RCS of three full-scale aircraft and the measured monotatic and bistatic RCS of four scale-model targets. The scattering centers are extracted using one-dimensional FFT-based CLEAN and then used as feature vectors for a neural network classifier. The results show that our method has better performance than algorithms that solely use monostatic or bistatic data.
Citation
Seung-Jae Lee, In-Sik Choi, Byunglae Cho, Edward J. Rothwell, and Andrew K. Temme, "Performance Enhancement of Target Recognition Using Feature Vector Fusion of Monostatic and Bistatic Radar," Progress In Electromagnetics Research, Vol. 144, 291-302, 2014.
doi:10.2528/PIER13103101
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