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Geodesic Normal Coordinate-Based Manifold Filtering for Target Detection
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DOI：10.1109/TGRS.2022.3183432.png)
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摘要
En 中文
Recently, the matrix information geometry (MIG) detector, which characterizes sample data as a Hermitian positive definite (HPD) matrix located on the HPD manifold, was rapidly developed and demonstrated extraordinary performance in numerous applications, especially in heterogeneous clutter backgrounds. In this article, the geodesic normal coordinate (GNC)-based manifold filter is proposed to improve the detection performance of the MIG detector in strong clutter backgrounds. Using the GNC system, the distribution of target echoes and clutter on the high-dimensional manifold can be visualized and analyzed. Moreover, by exploiting the information concerning the distribution of matrices, the manifold filter is proposed to enhance target echoes and suppress strong clutter. Then, the manifold-filter-based MIG detector is designed, and its superiority is theoretically analyzed. The actual clutter data are utilized to verify the effectiveness of the proposed method. The results show that the proposed manifold filter achieves a signal-to-clutter ratio improvement of more than 5 dB over the existing MIG detectors.
Keyword:
Clutter
Manifolds
Detectors
Covariance matrices
Object detection
Isosurfaces
Information geometry
Information geometry
matrix information geometry (MIG) detector
radar signal processing
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
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