QIAN Tong, CUI Wei, SHEN Qing. Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty[J]. Chinese Journal of Electronics, 2018, 27(2): 386-392. doi: 10.1049/cje.2017.08.016
Citation: QIAN Tong, CUI Wei, SHEN Qing. Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty[J]. Chinese Journal of Electronics, 2018, 27(2): 386-392. doi: 10.1049/cje.2017.08.016

Sparse Reconstruction Method for DOA Estimation Based on Dynamic Dictionary and Negative Exponent Penalty

doi: 10.1049/cje.2017.08.016
Funds:  This work is supported by the National Natural Science Foundation of China (No.61672097).
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  • Corresponding author: CUI Wei (corresponding author) received the B.S. degree in physics and Ph.D. degree in electronics engineering from Beijing Institute of Technology, Beijing, China, in 1998 and 2003, respectively. From March 2003 to March 2005, he worked as a post-doctoral researcher in the School of Electronic and Information Engineering, Beijing Jiaotong University.He has published more than 100 papers, held 17 patents, and received the Ministerial Level Technology Advancement Award thrice. (Email:cuiwei@bit.edu.cn)
  • Received Date: 2017-01-16
  • Rev Recd Date: 2017-05-03
  • Publish Date: 2018-03-10
  • This paper proposes a novel sparse representation method for direction of arrival estimation based on dynamic dictionary and negative exponent penalty. The dynamic dictionary can eliminate the off-grid effect and the negative exponent penalty is capable of strengthening the sparse constraint to improve the performance. The basis is regarded as a part of the optimal target and the cross iteration is utilized to jointly update the dictionary and sparse support in this method. Based on the propositions of the penalty function, the penalty function is designed to replace of l1 norm because of its unbiasedness and stronger sparse constraint. The regularization parameter is simplified as a constant due to pre-white process, which greatly extends the application range of the proposed method. The simulation results show that the proposed method can efficiently reduce the off-grid effect and the over-complete rate of the original dictionary. Compared with the conventional sparse representation methods, it has better performance and lower computation complexity.
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  • Q. Shen, W. Liu, W. Cui and S.L. Wu, "Underdetermined DOA estimation under the compressive sensing framework:A review", IEEE Access, Vol.4, pp.8865-8878, 2016.
    Q. Shen, W. Liu, W. Cui and S.L. Wu, "Extension of co-prime arrays based on the fourth-order difference co-array concept", IEEE Signal Processing Letters, Vol.23, No.5, pp.615-619, 2016.
    Y. Tian, Q.S. Lian and H. Xu, "DOA and polarization angle estimation algorithm based on sparse signal reconstruction", Acta Electronica Sinica, Vol.44, No.8, pp.1548-1556, 2016. (in Chinese)
    D. Malioutov, M. Cetin and A.S. Willsky, "A sparse signal reconstruction perspective for source localization with sensor arrays", IEEE Transactions on Signal Processing, Vol.53, No.8, pp.3010-3022, 2005.
    M. Aharon, M. Elad and A. Bruckstein, "K-SVD:An algorithm for designing overcomplete dictionaries for sparse representation", IEEE Transactions on Signal Processing, Vol.54, No.11, pp.4311-4322, 2006.
    I.T.I. Jovanovic, P. Frossard, M. Vetterli, et al., "Ultrasound tomography with learned dictionaries", 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.5502-5505, 2006.
    Z. Yang, L. Xie and C. Zhang, "Off-grid direction of arrival estimation using sparse bayesian inference", IEEE Transactions on Signal Processing, Vol.61, No.1, pp.38-43, 2013.
    Z. Yang, C. Zhang and L. Xie, "Robustly stable signal recovery in compressed sensing with structured matrix perturbation", IEEE Transactions on Signal Processing, Vol.60, No.9, pp.4658-4671, 2012.
    Z. Tan, P. Yang and A. Nehorai, Joint sparse recovery method for compressed sensing with structured dictionary mismatches, IEEE Transactions on Signal Processing, Vol.62, No.19, pp.4997-5008, 2014.
    Q. Shen, W. Cui, W. Liu, et al., "Underdetermined wideband DOA estimation of off-grid sources employing the difference coarray concept", Signal Processing, Vol.130, pp.299-304, 2017.
    Q. Shen, W. Cui, W. Liu, et al., "Low complexity directionof-arrival estimation based on wideband co-prime arrays", IEEE/ACM Transactions on Audio, Speech, Language Processing, Vol.23, No.9, pp.1445-1456, 2015.
    E.J. Candès and C. Fernandez-Granda, "Towards a mathematical theory of super-resolution", Communications on Pure and Applied Mathematics, Vol.67, No.6, pp.906-956, 2014.
    Z. Yang and L.H. Xie, "On gridless sparse methods for line spectral estimation from complete and incomplete data", IEEE Transactions on Signal Processing, Vol.63, No.12, pp.3139-3152, 2015.
    C.H. Zhang, "Nearly unbiased variable selection under minimax concave penalty", The Annals of Statistics, Vol.38, No.2, pp.894-942, 2010.
    F. Cao, M. Cai, Y. Tan, et al., "Image super-resolution via adaptive lp(0< p < 1) regularization and sparse representation", IEEE Transactions on Neural Networks and Learning Systems, Vol.27, No.7, pp.1550-1561, 2016.
    C. Ravazzi and E. Magli, "Fast and robust em-based IRLS algorithm for sparse signal recovery from noisy measurements", 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.3841-3845, 2015.
    Z.S. He, A. Cichocki, R. Zdunek, et al., "Improved FOCUSS method with conjugate gradient iterations", IEEE Transactions on Signal Processing, Vol.57, No.1, pp.399-404, 2009.
    A. Blake and A. Zisserman, "Visual reconstruction", Visual Reconstruction, MIT Press, USA, 1987.
    W.Y. Lei and B. Xiao. Chen, "High-resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning", Electronics Letters, Vol.51, No.3, pp.285-287, 2015.
    B. Wang, Z.H. Zhu and Y.W. Dai, "Direction of arrival estimation research for underwater acoustic target based on sparse bayesian learning with temporally correlated source", Acta Electronica Sinica, Vol.44, No.3, pp.693-698, 2016. (in Chinese)
    W. Cui, T. Qian and J. Tian, "Enhanced covariances matrix sparse representation method for DOA estimation", Electronics Letters, Vol.51, No.16, pp.1288-1290, 2015.
    P.L. Combettes and J.C. Pesquet, "Proximal splitting methods in signal processing", Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer-Verlag, New York, USA, pp.185-212, 2011.
    H.Y. Gao and A.G. Bruce, "Waveshrink with firm shrinkage", Statistica Sinica, Vol.7, pp.855-874, 1997.
    I.W. Selesnick and I. Bayram, "Sparse signal estimation by max-·imally sparse convex optimization", IEEE Transactions on Signal Processing, Vol.62, No.5, pp.1078-1092, 2014.
    Y. Sun, P. Babu and D.P. Palomar "Majorization-minimization algorithms in signal processing, communications, and machine learning", IEEE Transactions on Signal Processing, Vol.65, No.3, pp.794-816, 2017.
    J. Yin and T. Chen, "Direction-of-arrival estimation using a sparse representation of array covariance vectors", IEEE Transactions on Signal Processing, Vol.59, No.9, pp.4489-4493, 2011.
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