结合流形滤波的矩阵信息几何检测器
作者:
作者单位:

(1. 国防科技大学 气象海洋学院, 湖南 长沙 410073;2. 国防科技大学 电子科学学院, 湖南 长沙 410073)

作者简介:

华小强(1990—),男,湖北黄冈人,讲师,博士,E-mail:hxq712@yeah.net; 程永强(通信作者),男,教授,博士,博士生导师,E-mail:nudtyqcheng@gmail.com

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中图分类号:

TN911.23

基金项目:

国家自然科学基金资助项目(61901479)


Matrix information geometric detectors with manifold filter
Author:
Affiliation:

(1. College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China;2. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)

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    摘要:

    针对小样本、非均匀杂波下的信号检测问题,提出一种基于流形滤波的矩阵信息几何检测器,将信号检测问题转化为矩阵流形上的几何问题。将每一个样本的相关性数据建模为一个托普利兹正定矩阵,在此基础上,利用每一个样本数据的邻近矩阵进行加权平滑滤波,去除一部分杂波能量,提升目标与杂波间的区分性。计算了辅助样本数据对应矩阵的几何均值,通过比较待检测样本数据矩阵与几何均值矩阵之间的距离与检测门限的大小,以实现信号检测。实验结果表明,与自适应匹配滤波相比,本文方法在小样本、非均匀杂波下具有明显的性能优势。

    Abstract:

    Aiming at the problem of signal detection in small sample and nonhomogeneous clutter, a matrix information geometric detector based on manifold filter was proposed. The signal detection problem was transformed into a geometric problem on the matrix manifold. The correlation of each sample data was modeled as a Toeplitz positive definite matrix. On the basis, each matrix was replaced by a weighted smoothing filter of its surrounding matrices. The weighted smoothing filter removed part of the clutter energy and improved the discrimination between the target and the clutter. The geometric mean of a set of secondary sample data was calculated. By comparing the distance between the matrix under test and the geometric mean matrix with the detection threshold, the signal detection was realized. Simulation results demonstrate the superiority of the detection performance of the proposed method in small sample and nonhomogeneous clutter compared with adaptive matched filtering.

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引用本文

华小强,程永强,王宏强,等.结合流形滤波的矩阵信息几何检测器[J].国防科技大学学报,2022,44(6):51-60.
HUA Xiaoqiang, CHENG Yongqiang, WANG Hongqiang, et al. Matrix information geometric detectors with manifold filter[J]. Journal of National University of Defense Technology,2022,44(6):51-60.

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  • 收稿日期:2020-12-08
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  • 在线发布日期: 2022-12-01
  • 出版日期: 2022-12-28
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