A contrast-based phase calibration algorithm for digital beamforming remote sensing radars using three contrast metrics is presented. The algorithm corrects time-varying antenna array phase errors that defocus digital beamforming remote sensing radar imagery. Amplitude errors are treated by equalizing the received powers in all elements. As such, the algorithm does not produce an absolute (or radiometric) calibration vector for the array. The performance of the algorithm is studied using a combination of simulated and real radar data under various conditions and is compared with a clutter-based calibration algorithm. An analytical proof showing that maximizing the expected value of the 4-norm metric is equivalent to phase-calibrating the image, except for a linear phase offset, is provided. We find that the clutter calibration algorithm performs best for statistically homogeneous scenes but that the contrast-calibration algorithms perform better with scenes with larger contrast ratios.

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A contrast-based phase calibration algorithm for digital-beamforming radars using three contrast metrics is presented. The performance of the algorithm is studied using a combination of simulated and real radar data. We find that the entropy metric yields the smallest calibration vector, but that the M ₃ metric appears more robust in low SNR conditions.

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A minimum entropy based calibration algorithm for digitally beamformed imaging radars is presented. The algorithm is derived from work in SAR which corrects phase errors due to platform motion. The performance of the algorithm is compared to traditional techniques through both simulations and data from two imaging radars. The algorithm performs well with similar sidelobe levels for a point target. The results show that the minimum phase entropy algorithm produces a calibration vector without the need for a calibration target or for stringent scene statistics.@en