The spaceborne synthetic aperture radar (SAR) sparse flight 3-D imaging technology through multiple observations of the cross-track direction is designed to form the crosstrack equivalent aperture, and achieve the third dimensionality recognition.In this paper, combined with the actual triple star orbits, a sparse flight spaceborne SAR 3-D imaging method based on the sparse spectrum of interferometry and the principal component analysis (PCA) is presented.Firstly, interferometric processing is utilized to reach an effective sparse representation of radar images in the frequency domain.Secondly, as a method with simple principle and fast calculation, the PCA is introduced to extract the main features of the image spectrum according to its principal characteristics.Finally, the 3-D image can be obtained by inverse transformation of the reconstructed spectrum by the PCA.The simulation results of 4.84 km equivalent crosstrack aperture and corresponding 1.78 m cross-track resolution verify the effective suppression of this method on high-frequency sidelobe noise introduced by sparse flight with a sparsity of 49% and random noise introduced by the receiver.Meanwhile, due to the influence of orbit distribution of the actual triple star orbits, the simulation results of the sparse flight with the 7-bit Barker code orbits are given as a comparison and reference to illuminate the significance of orbit distribution for this reconstruction results.This method has prospects for sparse flight 3-D imaging in high latitude areas for its short revisit period.
The micro-Doppler modulation of the radar echo of the drone's rotor reflects the micro-movement characteristics of the target. Accurate estimation of the length and rotation frequency of an unmanned aerial vehicle (UAV) rotor is of great significance for target identification and classification in radar echoes. Firstly, this paper proposes a method of optimal estimation based on concentration of time-frequency rotation domain (CTFRD), in the time-frequency rotation domain of a multi-component micro-Doppler signal, under the FMCW radar system. Secondly, in the scene where the drone rotor rotates at a constant speed or at a uniform acceleration, the proposed method realizes the accurate estimation for multicomponent micro-motion feature parameters. Compared to traditional methods, it is also very robust in low signal-to-noise ratio (SNR) environments. Finally, the effectiveness of the proposed method is verified by simulations and real-world scenarios. Index Terms- Micro-Doppler, Concentration of time-frequency rotation domain, Parameter estimation, Target identification.
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