Iterative solution methods
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Source number estimators using transformed Gerschgorin radii
IEEE Transactions on Signal Processing
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We propose a novel, blind and robust algorithm, DELSA [5], to estimate the azimuthal direction-of-arrival (DOA) of sources impinging an array of antennas. Subsequently, we develop two new beam-steering algorithm for a typical frequency hopping (FH) system [4]. We treat the DOA estimation as an inverse problem of estimating a set of parameters {ϕi} which explains the received signal for a suitable physical model. We specify a highly underdetermined linear system as the physical model and solve the resulting least squares problem using the well-known Conjugate Gradients (CG) iterations on the Normal Equations (NE). Unlike DOA estimation methods such as MUSIC [1], our method does not require any a priori information concerning the number of sources for the algorithm to perform. It is also independent of the type of modulation employed [6]. Simulation results indicate that our method is robust in the regimes of friendly interferences and low signal-to-noise-ratios (SNRs) of the desired source.