BNC-based projection approximation subspace tracking under impulsive noise

Covariance, one of the most widely accepted mathematical tool for similarity measurement relies heavily on the assumption of Gaussian distribution noise model. Along with many other second-order statistics based methods, its performance deteriorates significantly in the presence of impulsive noise. Therefore, in this study, a generalised covariance function named bounded non-linear covariance (BNC) is put forward to handle relative problems in the presence of noise with non-Gaussian and heavy-tailed distribution. Meanwhile, the projection approximation subspace tracking-like algorithm based on BNC is proposed as well. Simulations have verified its performances over existing methods, especially the robustness to impulsive noise.

[1]  K. Dostert,et al.  Analysis and modeling of impulsive noise in broad-band powerline communications , 2002 .

[2]  Yixin Yin,et al.  Active control of SαS impulsive noise based on a sigmoid transformation algorithm , 2012, 2012 IEEE 11th International Conference on Signal Processing.

[3]  Johann F. Böhme,et al.  A pseudo-noise approach to direction finding , 1998, Signal Process..

[4]  Shing-Chow Chan,et al.  A robust past algorithm for subspace tracking in impulsive noise , 2006, IEEE Transactions on Signal Processing.

[5]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[6]  Carlos E. Davila,et al.  Efficient, high performance, subspace tracking for time-domain data , 2000, IEEE Trans. Signal Process..

[7]  Badong Chen,et al.  Maximum Correntropy Estimation Is a Smoothed MAP Estimation , 2012, IEEE Signal Processing Letters.

[8]  W. Gardner.  Exploitation of spectral redundancy in cyclostationary signals , 1991, IEEE Signal Processing Magazine.

[9]  Bin Yang,et al.  Asymptotic convergence analysis of the projection approximation subspace tracking algorithms , 1996, Signal Process..

[10]  Dan Hu,et al.  Cyclostationary Detection Based Spectrum Sensing for Cognitive Radio Networks , 2015, J. Commun..

[11]  Chrysostomos L. Nikias,et al.  The robust covariation-based MUSIC (ROC-MUSIC) algorithm for bearing estimation in impulsive noise environments , 1996, IEEE Trans. Signal Process..

[12]  Huijun Gao,et al.  Reduced-Order Generalized $H_{\infty}$ Filtering for Linear Discrete-Time Systems With Application to Channel Equalization , 2014, IEEE Transactions on Signal Processing.

[13]  Tho Le-Ngoc,et al.  Efficient Cooperative Cyclostationary Spectrum Sensing in Cognitive Radios at Low SNR Regimes , 2011, IEEE Transactions on Wireless Communications.

[14]  Nanning Zheng,et al.  Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion , 2014, IEEE Signal Processing Letters.

[15]  Xiaofei Zhang,et al.  Direction of Departure (DOD) and Direction of Arrival (DOA) Estimation in MIMO Radar with Reduced-Dimension MUSIC , 2010, IEEE Communications Letters.

[16]  Sergiy A. Vorobyov,et al.  Efficient Transmit Beamspace Design for Search-Free Based DOA Estimation in MIMO Radar , 2013, IEEE Transactions on Signal Processing.

[17]  Shing-Chow Chan,et al.  A New Robust Kalman Filter-Based Subspace Tracking Algorithm in an Impulsive Noise Environment , 2010, IEEE Transactions on Circuits and Systems II: Express Briefs.

[18]  S. V. Zhidkov,et al.  Impulsive noise suppression in OFDM-based communication systems , 2003, IEEE Trans. Consumer Electron..

[19]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[20]  Zongze Wu,et al.  Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion , 2015, Entropy.

[21]  Bin Yang,et al.  Projection approximation subspace tracking , 1995, IEEE Trans. Signal Process..