An off-gridsparse direction-of-arrival (doa) estimation algorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the doaestimation precision present in unknown spa...
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An off-gridsparse direction-of-arrival (doa) estimation algorithm, namely, iterative reweighted linear interpolation (IRLI), is proposed to avoid the declination of the doaestimation precision present in unknown spatial coloured noise. The authors start by developing an off-gridsparse model based on linear interpolation with reweighted coefficient, which is a trade-off between tangent and secant offset, to guarantee an optimal approximation for off-grid signals. Next, the authors formulate the doaestimationproblem as solving the off-gridsparse model and, finally, the off-gridsparse model is addressed under the general framework of sparse Bayesian learning (SBL). Additional noise in IRLI is spatially coloured for calculating its statistical properties, which is different from SBL relying on the spatial white noise assumption. Numerical results with the limited snapshots and the low signal-to-noise ratio validate the algorithm by comparing with other algorithms.
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