Sample selection based on direct estimation of cell under test clutter characteristics
Author:
Affiliation:

1.College of Engineering, Shanghai Polytechnic University, Shanghai 201209, China;2.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Clc Number:

TN958

Fund Project:

Supported by Subject Fund of Shanghai Polytechnic University

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    Abstract:

    This paper proposes a training sample selection algorithm for radar based on direct estimation of the CUT clutter characteristics. The proposed method directly uses the sub-aperture covariance matrix of CUT to characterize the clutter. Since the estimation process doesn't depend on training samples, the estimation of CUT is not affected by the outliers. Moreover, considering the existence of target signal in the CUT, the proposed method removes the target component from the sub-aperture covariance matrix of CUT based on clutter covariance matrix reconstruction, which utilizes the clutter Capon spectrum integrated over a sector separated from the location of target. Compared with the traditional generalized inner product algorithm which uses single snapshot to calculate the detection parameters, the new algorithm uses the sub-aperture covariance matrix of the samples to characterize its statistical characteristics, obtaining more stable results. The simulation results show that the proposed algorithm selects training samples more accurately.

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QIN Qin, TU Zi-Mei, LI Ming. Sample selection based on direct estimation of cell under test clutter characteristics[J]. Journal of Infrared and Millimeter Waves,2021,40(2):198~203

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History
  • Received:September 27,2020
  • Revised:April 02,2021
  • Adopted:November 02,2020
  • Online: March 30,2021
  • Published: