A ML method for TDOA and FDOA localization in the presence of receiver and calibration source location errors

This paper reveals through CRLB analysis that the use of calibration sources with position error degrades the efficiency of receiver location error restraining in real passive location systems. To improve the location accuracy, a kind of ML estimator by applying Newton iteration and alternative iteration which takes the uncertainty of the calibration source positions into account is developed. The proposed algorithm converges fast and is able to restrain moderate location errors of the receivers and the calibration sources. All the theoretical developments in this paper are corroborated by simulations.