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RSS FeedsRemote Sensing, Vol. 11, Pages 1415: Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems (Remote Sensing)


15 june 2019 00:00:57

Remote Sensing, Vol. 11, Pages 1415: Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems (Remote Sensing)

Multi-channel beam-steering synthetic aperture radar (multi-channel BS-SAR) can achieve high resolution and wide-swath observations by combining beam-steering technology and azimuth multi-channel technology. Various imaging algorithms have been proposed for multi-channel BS-SAR but the associated parameter estimation and error calibration have received little attention. This paper focuses on errors in the main parameters in multi-channel BS-SAR (the derotation rate and constant Doppler centroid) and phase inconsistency errors. These errors can significantly reduce image quality by causing coarser resolution, radiometric degradation, and appearance of ghost targets. Accurate derotation rate estimation is important to remove the spectrum aliasing caused by beam steering, and spectrum reconstruction for multi-channel sampling requires an accurate estimate of the constant Doppler centroid and phase inconsistency errors. The time shift and scaling effect of the derotation error on the azimuth spectrum are analyzed in this paper. A method to estimate the derotation rate is presented, based on time shifting, and integrated with estimation of the constant Doppler centroid. Since the Doppler histories of azimuth targets are space-variant in multi-channel BS-SAR, the conventional estimation methods of phase inconsistency errors do not work, and we present a novel method based on minimum entropy to estimate and correct these errors. Simulations validate the proposed error estimation methods. Digg Facebook Google StumbleUpon Twitter
24 viewsCategory: Geology, Physics
Remote Sensing, Vol. 11, Pages 1416: Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1414: Region Merging Method for Remote Sensing Spectral Image Aided by Inter-Segment and Boundary Homogeneities (Remote Sensing)
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