P. Gerstoft, C. Mecklenbräuker, S. Nannuru, G. Leus:

"DOA Estimation in Heteroscedastic Noise with Sparse Bayesian Learning";

Applied Computational Electromagnetics Society Journal,35(2020), 11; 1439 - 1440.

With long observation times, parameters of weak signals can be estimated in a noisy environment. Most analytic treatments analyze these cases assuming Gaussian noise with constant variance. For long observation times the noise process is likely to change with time caused by evolving noise variance. This is called a heteroscedastic Gaussian process. While the noise variance is a nuisance parameter that we are not interested in, it still needs to be estimated or included in the processing in order to obtain an accurate estimate of the parameters of the weak signals.

In this paper we resolve closely spaced weak sources when the noise power is varying in space and time. Specifically, we derive noise variance estimates and demonstrate this for compressive beamforming using multiple measurement vectors (MMV or multiple snapshot). We solve the MMV problem using the sparse Bayesian learning (SBL) framework.

https://publik.tuwien.ac.at/files/publik_283517.pdf

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