Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Error Correction Coding: Mathematical Methods and Algorithms
Error Correction Coding: Mathematical Methods and Algorithms
Experimental performance of calibration and direction-finding algorithms
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Model order selection for short data: an exponential fitting test (EFT)
EURASIP Journal on Applied Signal Processing
Direction finding in partly calibrated sensor arrays composed of multiple subarrays
IEEE Transactions on Signal Processing
Gain calibration methods for radio telescope arrays
IEEE Transactions on Signal Processing
Computationally efficient maximum likelihood estimation ofstructured covariance matrices
IEEE Transactions on Signal Processing
Self-Calibration for the LOFAR Radio Astronomical Array
IEEE Transactions on Signal Processing
Spatial signature estimation for uniform linear arrays with unknownreceiver gains and phases
IEEE Transactions on Signal Processing
Direction-of-arrival estimation in partly calibrated subarray-based sensor arrays
IEEE Transactions on Signal Processing
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Calibration of a sensor array is more involved if the antennas have direction dependent gains and multiple calibrator sources are simultaneously present. We study this case for a sensor array with arbitrary geometry but identical elements, i.e., elements with the same direction dependent gain pattern. A weighted alternating least squares (WALS) algorithm is derived that iteratively solves for the direction independent complex gains of the array elements, their noise powers and their gains in the direction of the calibrator sources. An extension of the problem is the case where the apparent calibrator source locations are unknown, e.g., due to refractive propagation paths. For this case, the WALS method is supplemented with weighted subspace fitting (WSF) direction finding techniques. Using Monte Carlo simulations we demonstrate that both methods are asymptotically statistically efficient and converge within two iterations even in cases of low SNR.