Clutter Suppression and Rotor Blade Feature Extraction of a Helicopter Based on Time–Frequency Flash Shifts in a Passive Bistatic Radar

This paper presents a passive bistatic radar (PBR) configuration using a global navigation satellite system as an illuminator of opportunity for the rotor blade feature extraction of a helicopter. Aiming at the strong fixed clutter in the surveillance channel of the PBR, a novel iteration clutter el...

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Bibliographic Details
Published inAtmosphere Vol. 13; no. 8; p. 1214
Main Authors Zhou, Zibo, Wang, Zhihui, Wang, Binbin, Xia, Saiqiang, Liu, Jianwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
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Summary:This paper presents a passive bistatic radar (PBR) configuration using a global navigation satellite system as an illuminator of opportunity for the rotor blade feature extraction of a helicopter. Aiming at the strong fixed clutter in the surveillance channel of the PBR, a novel iteration clutter elimination method-based singular-value decomposition approach is proposed. Instead of the range elimination method used in the classic extended cancellation algorithm, the proposed clutter elimination method distinguishes the clutter using the largest singular value and by remove this value. At the same time, the fuselage echo of the hovering helicopter can also be suppressed along with the ground clutter, then the rotor echo of this can be obtained. In the micro-motion feature extraction, the mathematic principle of the flash generation process in the time–frequency distribution (TFD) is derived first. Next, the phase compensation method is applied to achieve the time–frequency flash shift in the TFD. After this, the center frequencies of the standard flashes in the TFD are compared with the standard frequency dictionary. The mean l1 norm is utilized to estimate the feature parameters of the helicopter rotor. In the experiments, the scattering point model and the physical optics facet model demonstrate that the proposed method can obtain more accurate parameter estimation results than some classic algorithms.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos13081214