Knowledge-aided array calibration for registration-based range-dependence compensation in airborne STAP radar with Conformal Antenna Arrays

We consider space-time adaptive processing (STAP) when the radar returns are recorded by a conformal antenna array (CAA). The statistics of the secondary data snapshots used to estimate the optimum weight vector are not identically distributed with respect to range, thus preventing the customary STAP processor from achieving its optimum performance. The compensation of the range-dependence of the secondary data requires the precise knowledge of the space-time steering vector. We propose a new knowledge-aided method based on the eigenstructure of the space-time covariance matrix for calibrating the gain and phase of each sensor in the CAA. Based on the calibrated space-time steering vectors, we can perform an accurate range-dependence compensation to obtain a valid estimate of the covariance matrix. End-to-end performance analysis in terms of signal to inference-plus-noise ratio loss shows that the method yields promising performance.