Experimental antenna array calibration with artificial neural networks
Signal Processing
Robust approaches to remote calibration of a transmitting array
Signal Processing
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This paper presents an iterative algorithm for estimating the signal steering vectors and associated power levels received by an array of uncalibrated isotropic sensors. The inputs are assumed to consist of narrowband, uncorrelated directional signals in the presence of additive white noise. An iterative algorithm is employed to successively search for estimates that simultaneously satisfy a signal subspace and an orthogonality condition. These conditions are shown to be both necessary and sufficient for identification of the underlying steering vectors in the case when the data covariance matrix is known exactly, i.e. for the case of infinite data observation. The iterative method employs minimum distance criterion (projections) to successively map the solution between three constraint sets until a stable point is determined. Two examples are presented which illustrate the application of the algorithm in direction finding and beamforming