The generalized spatial correlation algorithm for self-calibration of microwave antenna arrays

High resolution microwave imaging systems generally require the use of large aperture array antennas. Such large aperture systems inevitably experience phase errors in the measured data. The Spatial Correlation class of algorithms is one class of algorithms that attempts to remove these phase errors from the recorded data set. This work develops a general algorithm (GSCA) that characterizes this class and allows its characteristics and properties to be studied. The generalized theory is applied to successfully self-calibrate experimental data sets that could not be calibrated successfully with existing theory. The GSCA reveals that the Spatial Correlation class is divided into two sub-classes of algorithms. Extensive simulations are used to compare performances of algorithms from each sub-class in the presence of receiver noise and element position errors. The performance curves presented as a result of the performance study represent the first such set of performance curves available and can be used as design curves for system designers. In the last chapter the GSCA is extended for self-calibration with near-field data.