文件列表:

Matlab_code_CCS-MMV (0, 2014-09-10)
Matlab_code_CCS-MMV\ANM_sdpt3.m (1987, 2014-09-13)
Matlab_code_CCS-MMV\test_ANM.m (849, 2014-09-10)
Matlab_code_CCS-MMV\VanDec.m (1366, 2014-09-13)

Thank you for your interest in this work and downloading this code. The main function ANM_sdpt3.m implements the atomic norm minimization problem via duality using SDPT3. References: [1] Z. Yang and L. Xie, "Continuous Compressed Sensing With a Single or Multiple Measurement Vectors", IEEE Workshop on Statistical Signal Processing (SSP), pp. 308--311, June 2014. [2] Z. Yang and L. Xie, "Exact joint sparse frequency recovery via optimization methods", http://arxiv.org/abs/1405.6585, May 2014. test_OGSBI.m is a test file. Other related works of the authors include [1] Z. Yang, L. Xie, and C. Zhang, "Off-grid direction of arrival estimation using sparse Bayesian inference", IEEE Trans. Signal Processing, vol. 61, no. 1, pp. 38--43, 2013. [2] Z. Yang, C. Zhang, and L. Xie, "Robustly stable signal recovery in compressed sensing with structured matrix perturbation", IEEE Trans. Signal Processing, vol. 60, no. 9, pp. 4658--4671, 2012. [3] Z. Yang, L. Xie, and C. Zhang, "A discretization-free sparse and parametric approach for linear array signal processing", IEEE Trans. Signal Processing, vol. 62, no. 19, pp. 4959--4973, 2014. [4] Z. Yang and L. Xie, "On gridless sparse methods for line spectral estimation from complete and incomplete data", http://arxiv.org/abs/1407.2490, May 2014. [5] Z. Yang and L. Xie, "Enhancing sparsity and resolution via reweighted atomic norm minimization," http://arxiv.org/abs/1408.5750, Aug 2014. In [1], a fast and accurate algorithm in presented for off-grid DOA estimation. In [2], the off-grid DOA estimation problem is generalized and analyzed in the framework of compressed sensing. We provide some theoretic guarantees and demonstrate its application to DOA estimation. In [3], a discretization-free sparse method is firstly ever introduced for continuous DOA estimation which completely eliminates basis mismatches in existing discretization-based methods. In [4], we present a systematic discretization-free/gridless sparse method for continuous frequency estimation with automatic model order selection, and analyze its connection to existing atomic norm-based methods. In [5], we propose a reweighted atomic norm minimization method to achieve high resolution and sparsity for gridless frequency estimation. The Matlab codes of the above papers have been or will be published on my personal website when available.

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