Robust DOA Estimation in the Presence of Miscalibrated Sensors

In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is adaptively optimized and applied to each sensor in order to effectively mitigate the effect of the outliers. An algorithm based on the maximum correntropy criterion is then developed to yield robust DOA estimation. The simulation results are presented to verify the effectiveness and superiority of the proposed approach compared with conventional DOA estimation algorithms.

[1]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[2]  Ben Wang,et al.  Robust group compressive sensing for DOA estimation with partially distorted observations , 2016, EURASIP J. Adv. Signal Process..

[3]  Dazhuan Xu,et al.  Low-complexity ESPRIT-based DOA estimation for colocated MIMO radar using reduced-dimension transformation , 2011 .

[4]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[5]  Braham Himed,et al.  Complex multitask Bayesian compressive sensing , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Yimin Zhang,et al.  Robust DOA Estimation in the Presence of Miscalibrated Sensors , 2017, IEEE Signal Process. Lett..

[7]  Ilan Ziskind,et al.  Maximum likelihood localization of multiple sources by alternating projection , 1988, IEEE Trans. Acoust. Speech Signal Process..

[8]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[9]  Zhongfu Ye,et al.  A Hadamard Product Based Method for DOA Estimation and Gain-Phase Error Calibration , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[10]  B. Friedlander,et al.  Eigenstructure methods for direction finding with sensor gain and phase uncertainties , 1990 .

[11]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[12]  Sergiy A. Vorobyov,et al.  Maximum likelihood direction-of-arrival estimation in unknown noise fields using sparse sensor arrays , 2005, IEEE Transactions on Signal Processing.

[13]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[14]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.

[15]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[16]  Junli Liang,et al.  Robust Ellipse Fitting via Half-Quadratic and Semidefinite Relaxation Optimization , 2015, IEEE Transactions on Image Processing.

[17]  Bhaskar D. Rao,et al.  Performance analysis of Root-Music , 1989, IEEE Trans. Acoust. Speech Signal Process..

[18]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[19]  Bin Liao,et al.  Direction Finding With Partly Calibrated Uniform Linear Arrays , 2012, IEEE Transactions on Antennas and Propagation.

[20]  P. Rocca,et al.  Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies , 2013, IEEE Transactions on Antennas and Propagation.

[21]  Braham Himed,et al.  Sparsity-based DOA estimation using co-prime arrays , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Yimin Zhang,et al.  Generalized Coprime Array Configurations for Direction-of-Arrival Estimation , 2015, IEEE Transactions on Signal Processing.

[23]  Bao-Gang Hu,et al.  Robust feature extraction via information theoretic learning , 2009, ICML '09.

[24]  Dong Wang,et al.  Robust MIMO radar target localization via nonconvex optimization , 2016, Signal Process..

[25]  Yimin Zhang,et al.  DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar , 2017, Digit. Signal Process..

[26]  B. Friedlander,et al.  DOA and steering vector estimation using a partially calibrated array , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[27]  Moeness G. Amin,et al.  DOA estimation of mixed coherent and uncorrelated signals exploiting a nested MIMO system , 2014, 2014 IEEE Benjamin Franklin Symposium on Microwave and Antenna Sub-systems for Radar, Telecommunications, and Biomedical Applications (BenMAS).

[28]  Lihua Xie,et al.  On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data , 2014, IEEE Transactions on Signal Processing.

[29]  Xiaofei Zhang,et al.  Reduced-Dimension MUSIC for Angle and Array Gain-Phase Error Estimation in Bistatic MIMO Radar , 2013, IEEE Communications Letters.

[30]  J. Capon High-resolution frequency-wavenumber spectrum analysis , 1969 .

[31]  Thomas Kailath,et al.  ESPIRT-estimation of signal parameters via rotational invariance techniques , 1989 .

[32]  Yimin Zhang,et al.  Doa estimation of nonparametric spreading spatial spectrum based on bayesian compressive sensing exploiting intra-task dependency , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[33]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[34]  Thomas Kailath,et al.  ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[35]  Braham Himed,et al.  Multi-Task Bayesian Compressive Sensing Exploiting Intra-Task Dependency , 2015, IEEE Signal Processing Letters.