Codes for "Source Localization for Sparse Array using Nonnegative Sparse Bayesian Learning"

Versione 1.0.0.0 (10,2 KB) da Nan Hu
The main codes of the paper published in Signal Processing
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Aggiornato 19 feb 2016

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This work is to address the problem of source localization for sparse arrays, by formulating a nonnegative sparse signal recovery (SSR) problem and developing a nonnegative sparse Bayesian learning (NNSBL) algorithm.
1. The proposed algorithm is given in 'NNSBL.m', and the conventional SBL algorithm is given in 'Conven_SBL.m' for comparison.
2. 'MRA_output.m' is used to generate the array output data, and 'Peaksearch.m' and 'peak_find.m' are used to find the locations of the peaks in the spatial spectrum.
3. 'Main_Simulation.m' is used to display the spatial spectrum.
4. 'rmse_snr.m' is used to display the RMSE of DOA estimation versus SNR.
5. 'rmse_snapshot.m' is used to display the RMSE of DOA estimation versus the number of snapshots.

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Nan Hu (2026). Codes for "Source Localization for Sparse Array using Nonnegative Sparse Bayesian Learning" (https://it.mathworks.com/matlabcentral/fileexchange/55488-codes-for-source-localization-for-sparse-array-using-nonnegative-sparse-bayesian-learning), MATLAB Central File Exchange. Recuperato .

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