Two-Step Esprit with Compensation for Modelling Errors using a Sparse Calibration Grid

In arrays with scan dependent errors, such as large position errors, a dense calibration grid can become necessary. Calibration time is, however, very expensive and keeping the measured calibration grid as sparse as possible is important. In this paper it is shown how interpolation using local models can be used to make the calibration grid more dense without increasing the number of measurements. Furthermore, it is shown how the performance of the DOA estimation with ESPRIT using arrays with large position errors can be improved by a second step including weighted calibration.