A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing

Direction of arrival (DOA) estimation in array processing using uniform/sparse linear arrays is concerned in this paper. While sparse methods via approximate parameter discretization have been popular in the past decade, the discretization may cause problems, e.g., modeling error and increased compu...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 62; no. 19; pp. 4959 - 4973
Main Authors Yang, Zai, Xie, Lihua, Zhang, Cishen
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Array processing
Arrays
compressed sensing
Computer simulation
continuous parameter estimation
Direction-of-arrival estimation
Discretization
DOA estimation
Economic models
Estimation
Fittings
Linear arrays
Mathematical models
Methods
Noise
Parameter estimation
Redundancy
Sampling
Signal processing
sparse and parametric approach (SPA)
Vectors
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ISSN1053-587X
1941-0476
DOI10.1109/TSP.2014.2339792

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Summary:Direction of arrival (DOA) estimation in array processing using uniform/sparse linear arrays is concerned in this paper. While sparse methods via approximate parameter discretization have been popular in the past decade, the discretization may cause problems, e.g., modeling error and increased computations due to dense sampling. In this paper, an exact discretization-free method, named as sparse and parametric approach (SPA), is proposed for uniform and sparse linear arrays. SPA carries out parameter estimation in the continuous range based on well-established covariance fitting criteria and convex optimization. It guarantees to produce a sparse parameter estimate without discretization required by existing sparse methods. Theoretical analysis shows that the SPA parameter estimator is a large-snapshot realization of the maximum likelihood estimator and is statistically consistent (in the number of snapshots) under uncorrelated sources. Other merits of SPA include improved resolution, applicability to arbitrary number of snapshots, robustness to correlation of the sources and no requirement of user-parameters. Numerical simulations are carried out to verify our analysis and demonstrate advantages of SPA compared to existing methods.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2014.2339792