Abstract
Polarimetric high resolution range profile (HRRP) contains the geometrical structural information along radar line-of-sight and has shown great potentials in target recognition. In existing researches, H/α decomposition has been applied to exploit global polarimetric features of a single HRRP from its spatially averaged coherent matrix, which loses target local scattering information. In this paper, we propose to apply the H/α decomposition along the slow time dimension in a dual polarimetric HRRP sequence. The H and α features are extracted for each target scattering center by averaging its samples in different observations of the HRRP sequence, which provides both target local high resolution and polarimetric scattering characteristics. We also design a novel six-zone H-α plane for geometrical structure classification of target scattering centers. Simulation and experimental results show the effectiveness of the proposed method and display its good potentials for practical applications.
Highlights
High resolution range profile (HRRP) represents the distribution of target scattering centers along radar line-of-sight (LOS)
We propose a geometrical structure classification method of target scattering centers based on H /α decomposition technique using dual polarimetric HRRP sequence
We focus on discussing the effect of white noise on H and α features and its impact on geometrical structure classification using the H -α plane, which is a useful tool for target classification [22], [41]
Summary
High resolution range profile (HRRP) represents the distribution of target scattering centers along radar line-of-sight (LOS). We propose a geometrical structure classification method of target scattering centers based on H /α decomposition technique using dual polarimetric HRRP sequence. Our research is based on a slant 45◦ dual polarimetric mode, in which a 45◦ linear polarization is transmitted followed by reception in orthogonal 45◦ and 135◦ polarizations simultaneously We use this mode because it has advantage of low system complexity and good recognition performance for certain typical geometrical structures [26].
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