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

• Published in: IET signal processing Vol. 11; no. 4; pp. 429 - 436
• Main Authors: Mahnaz, Amiri Parian, Sedigheh, Ghofrani
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.06.2017
• Subjects: array processing; array signal processing; coherent sources; compressed sensing; compressive sensing; direction of arrival estimation; DOA estimation; higher order polynomial sources; hybrid method; iterative methods; matching pursuit; MP decomposition coefficients; multiple measurement vector recovery algorithms; multiple signal classification; Mℓ1,2 ‐MUSIC algorithm; nonlinear frequency characteristic; nonstationary sources; polynomials; Research Article; signal classification; signal decomposition; small‐sized sensor array; sparse model; multiple measurement vector recovery algorithms; iterative methods; array processing; compressed sensing; hybrid method; polynomials; MP decomposition coefficients; signal decomposition; nonlinear frequency characteristic; direction-of-arrival estimation; matching pursuit; signal classification; DOA estimation; multiple signal classification; nonstationary sources; small-sized sensor array; direction of arrival estimation; coherent sources; sparse model; compressive sensing; higher order polynomial sources; array signal processing; Mℓ1,2-MUSIC algorithm
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2016.0246

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.