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2017-09-22
Positive Definite Matrix Space Based Detector with Limited Training Samples for Multiple Target Situations
By
Wen Jiang,

    Dr. Wen Jiang

    School of Electronic Engineering

    University of Electronic Science and Technology of China

    China

    Homepage

Yulin Huang,

    Dr. Yulin Huang

    School of Electronic Engineering

    University of Electronic Science and Technology of China

    China

    Homepage

Guolong Cui,

    Dr. Guolong Cui

    School of Electronic Engineering

    University of Electronic Science and Technology of China

    China

    Homepage

Jianyu Yang

    Dr. Jianyu Yang

    School of Electronic Engineering

    University of Electronic Science and Technology of China

    China

    Homepage

Progress In Electromagnetics Research M, Vol. 60, 141-156, 2017
doi:10.2528/PIERM17062003 | Google Scholar

Abstract
Multiple target situation is a typical situation of nonhomogeneous clutter environment, which can cause excessive target masking in radar signal detection system. In order to reduce target masking caused by multiple target situations, this paper proposes a new detection structure based on positive-de nite matrix space and limited training samples. The proposed detection structure uses a positive-de nite matrix to estimate the background power level. In addition, with limited training samples, the detection structure is used to resist the multiple target situations. The simulation results show that the proposed detection structure exhibits a better detection performance than that of the well-known CA-CFAR in homogeneous environment. The detector also performs robustly in multiple target situations, even though 10 interfering targets exist in a length of 24 samples of reference window. Furthermore, the measured results validate the performance of the proposed method.
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Citation
Wen Jiang, Yulin Huang, Guolong Cui, and Jianyu Yang, "Positive Definite Matrix Space Based Detector with Limited Training Samples for Multiple Target Situations," Progress In Electromagnetics Research M, Vol. 60, 141-156, 2017.
doi:10.2528/PIERM17062003
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