Bayesian probability

Combinatorics

Undersampling

Pattern recognition (psychology)

Mathematics

Beamforming

Algorithm

Sparse array

Prime (order theory)

Artificial intelligence

Bayesian inference

Set (abstract data type)

Programming language

Aliasing

Computer science

Telecommunications

The Journal of the Acoustical Society of America

Sparse linear arrays such as co-prime and nested arrays can resolve more sources than the number of sensors. In contrast, uniform linear arrays (ULA) cannot resolve more sources than the number of sensors. This paper demonstrates this using Sparse Bayesian learning (SBL) and co-array MUSIC for single frequency beamforming. For approximately the same number of sensors, co-prime and nested arrays are shown to outperform ULA in root mean squared error. This paper shows that multi-frequency SBL can significantly reduce spatial aliasing. The effects of different sparse sub-arrays on SBL performance are compared qualitatively using the Noise Correlation 2009 experimental data set.

Sparse Bayesian learning for beamforming using sparse linear arrays