Unmanned Aerial Vehicle ( UAV) synthetic aperture radar (SAR) is crucial for battlefield perception due to its advantages of small size and low cost. However, motion errors are higher in UAV SAR systems because of atm...
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ISBN:
(纸本)9798350365719;9798350365726
Unmanned Aerial Vehicle ( UAV) synthetic aperture radar (SAR) is crucial for battlefield perception due to its advantages of small size and low cost. However, motion errors are higher in UAV SAR systems because of atmospheric turbulence and the small size of the UAV. Additionally, it is challenging to equip high-precision inertial navigation devices due to the limited payload capacity of UAVs. Therefore, there is a need to extract motion parameters from raw radar data. In this paper, we propose a method that involves dividing the range and azimuth spectrum into blocks and estimating the minimum entropy for each block to extract motion errors. By fitting these motion errors, we can correct for spatially varying range migration and phase errors. Experimental results demonstrate the effectiveness of this method for UAV SAR systems.
motioncompensation is a key procedure in inverse synthetic aperture radar (ISAR) imaging. The authors regard the motioncompensation as a multi-parameter estimation problem. Based on the designing structured Gram mat...
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motioncompensation is a key procedure in inverse synthetic aperture radar (ISAR) imaging. The authors regard the motioncompensation as a multi-parameter estimation problem. Based on the designing structured Gram matrices optimisation method, a novel motioncompensation method is presented. This method consists of two parts: the range alignment algorithm and the phase compensationalgorithm. The former estimates the offset of each range profile by using a criterion, which makes correlations among all of the range profiles approaching to maximum values simultaneously. Also, the latter can extract phase errors by using the optimisation method to approach the ideal optimal matrix which is derived from analysis on the signal model. The measured data processing results show that the novel motioncompensation method has strong robustness and high estimation accuracy.
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