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Mℓ1,2-MUSIC algorithm for DOA estimation of coherent sources

Mℓ1,2-MUSIC algorithm for DOA estimation of coherent sources

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Recasting the direction of arrival (DOA) estimation problem into sparse model is a subject of many researches, which has been carried out by different methods. Using the compressive sensing (CS) multiple measurement vector recovery algorithms have improved the resolution of DOA estimation in comparison with conventional methods such as multiple signal classification (MUSIC). In addition, a recently introduced hybrid method using CS and array processing named as ℓ1,2-MUSIC exhibits high resolution as well as capability of resolving coherent sources. However, with recent advances in technology and employing higher order polynomial sources due to non-linear frequency characteristic, the performance of conventional methods is poor when the size of the array is small or the noise level is high. A novel hybrid method is presented based on matching pursuit (MP) signal decomposition and the ℓ1,2-MUSIC algorithm. The new approach named Mℓ1,2-MUSIC takes MP decomposition coefficients of the signal into account and applies the ℓ1,2-MUSIC to achieve DOA estimation. Along with improving the resolvability of closely located non-stationary sources (high ordered polynomial phase) with small-sized sensor array, the main advantage of the proposed method is robustness to source coherency.

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