Presence Of Impulsive Noise Research Articles

Bayesian Sparse Reconstruction Method of Compressed Sensing in the Presence of Impulsive Noise

The majority of existing recovery algorithms in the framework of compressed sensing are not robust to the impulsive noise. However, the impulsive noise is always present in the actual communication and signal processing system. In this paper, we propose a method named ‘Bayesian sparse reconstruction’ to recover the sparse signal from the measurement vector which is corrupted by the impulsive noise. The Bayesian sparse reconstruction method is composed of five parts, which are the preliminary detection of the location set of impulses, the impulsive noise fast relevance vector machine algorithm, the step of pruning, Bayesian impulse detection algorithm and the maximum a posteriori estimate of the sparse vector. The Bayesian sparse reconstruction method can achieve effective signal recovery in the presence of impulsive noise, depending on the mutual influence of the impulsive noise fast relevance vector machine algorithm, the step of pruning and the Bayesian impulse detection algorithm. Experimental results show that the Bayesian sparse reconstruction method is robust to the impulsive noise and effective in the additive white Gaussian noise environment.

L-statistics based modification of reconstruction algorithms for compressive sensing in the presence of impulse noise

A modification of standard compressive sensing algorithms for sparse signal reconstruction in the presence of impulse noise is proposed. The robust solution is based on the L-estimate statistics which is used to provide appropriate initial conditions that lead to improved performance and efficient convergence of the reconstruction algorithms.

A New Variable Regularized QR Decomposition-Based Recursive Least M-Estimate Algorithm—Performance Analysis and Acoustic Applications

This paper proposes a new variable regularized QR decompPosition (QRD)-based recursive least M-estimate (VR-QRRLM) adaptive filter and studies its convergence performance and acoustic applications. Firstly, variable L2 regularization is introduced to an efficient QRD-based implementation of the conventional RLM algorithm to reduce its variance and improve the numerical stability. Difference equations describing the convergence behavior of this algorithm in Gaussian inputs and additive contaminated Gaussian noises are derived, from which new expressions for the steady-state excess mean square error (EMSE) are obtained. They suggest that regularization can help to reduce the variance, especially when the input covariance matrix is ill-conditioned due to lacking of excitation, with slightly increased bias. Moreover, the advantage of the M-estimation algorithm over its least squares counterpart is analytically quantified. For white Gaussian inputs, a new formula for selecting the regularization parameter is derived from the MSE analysis, which leads to the proposed VR-QRRLM algorithm. Its application to acoustic path identification and active noise control (ANC) problems is then studied where a new filtered-x (FX) VR-QRRLM ANC algorithm is derived. Moreover, the performance of this new ANC algorithm under impulsive noises and regularization can be characterized by the proposed theoretical analysis. Simulation results show that the VR-QRRLM-based algorithms considerably outperform the traditional algorithms when the input signal level is low or in the presence of impulsive noises and the theoretical predictions are in good agreement with simulation results.

Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

Additive asynchronous and cyclostationary impulsive noise limits communication performance in OFDM powerline communication (PLC) systems. Conventional OFDM receivers assume additive white Gaussian noise and hence experience degradation in communication performance in impulsive noise. Alternate designs assume a parametric statistical model of impulsive noise and use the model parameters in mitigating impulsive noise. These receivers require overhead in training and parameter estimation, and degrade due to model and parameter mismatch, especially in highly dynamic environments. In this paper, we model impulsive noise as a sparse vector in the time domain without any other assumptions, and apply sparse Bayesian learning methods for estimation and mitigation without training. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones to estimate and subtract the noise impulses; (2) we add the information in the data tones to perform joint noise estimation and OFDM detection; (3) we embed our algorithm into a decision feedback structure to further enhance the performance of coded systems. When compared to conventional OFDM PLC receivers, the proposed receivers achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in the presence of impulsive noise.

Robust Recursive Beamforming in the Presence of Impulsive Noise and Steering Vector Mismatch

This paper proposes a new method for robust beamforming in the presence of impulsive noise as well as steering vector mismatch. In our proposed method, the idea of M-estimation is firstly incorporated into the traditional orthonormal PAST (OPAST) algorithm for subspace tracking to combat the hostile effect of impulsive noise. Taking advantage of the subspace principle, we show that the steering vector mismatch can be recursively and robustly estimated in closed form. Then, by making use of the estimated steering vector, the problem of robust beamforming in the presence of impulsive noise is formulated. The solution of this problem is analytically derived and the resultant robust beamformer is shown to have a similar form to the Capon beamformer, whereas the array covariance matrix and the steering vector are robustly estimated. Different from conventional methods, the impulsive noise and the steering vector mismatch are simultaneously handled by extending the traditional OPAST algorithm, and hence the proposed method has low complexity and it is feasible to nonstationary scenarios with moving sources. Simulation results demonstrate the validity and superiority of the proposed method over conventional methods in impulsive noise environment with steering vector mismatch.

A Cyclic Correlation-Based Blind SINR Estimation for OFDM Systems

A blind signal-to-interference-plus-noise ratio (SINR) estimation method is proposed for orthogonal frequency division multiplexing (OFDM) systems in the presence of impulsive noise over multi-path fading channels. By exploiting the cyclostationarity of the OFDM signal, the received signal power is decomposed into the transmitted signal power and the impulsive interference plus the Gaussian noise power, which are estimated respectively using the cyclic correlations of the received signal. Simulation results are provided to examine the performance of the proposed estimation method and show that the estimated results approach the true values of SINR.

An Investigation of Block Coding for Laplacian Noise

In many communication channels, the ambient disturbance is known through experimental evidence as well as theoretical considerations to deviate strongly from the Gaussian model. In order to evaluate the performance of digital signaling in such channels, the non-Gaussian noise must be properly modeled. The Laplace distribution is used here to model the non-Gaussian noise and study the performance of communication systems employing block data transmission in the presence of impulsive noise. Analytical expressions for assessing codeword error probability and bit error probability are derived for hard-decision decoders, soft-decision decoders and optimum maximum likelihood detectors in both nonfading and quasi-static Rayleigh fading scenarios. Numerical results are presented for different codes, block sizes and error rate levels. The characteristics of error performance are discussed for both nonfading and quasi-static Rayleigh fading channels.

Impulsive noise rejection method for compressed measurement signal in compressed sensing

Abstract The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

Effect of Impulse Noise on Wireless Relay Channel

Relay Network is most suited wireless communication technique for future Smart Grid, because it has a lot of advantages such as reliability against failures and offers redundancy, self-configuring and self-healing. For example if one nod or path is blocked, it can find a neighbour node, by different algorithms schemes. The purpose of this paper is to simulate wireless relay channel model in presence of impulse noise and computing the BER performance of channel data. It makes to be useful for a designer to predicting error and behaviour of a channel for optimum design, to be able choose a robust channel.

Impacts of impulsive noise from partial discharges on wireless systems performance: application to MIMO precoders

Abstract To satisfy the smart grid electrical network, communication systems in high-voltage substations have to be installed in order to control equipments. Considering that those substations were not necessarily designed for adding communication networks, one of the most appropriate solutions is to use wireless sensor network (WSN). However, the high voltage transported through the station generates a strong and specific radio noise. In order to prepare for such a network, the electromagnetic environment has to be characterized and tests in laboratories have to be performed to estimate the communication performances. This paper presents a method for measuring the noise due to high voltage and more particularly the impulsive noise. In the laboratory, we generate the impulsive noise using two specimens, and we show that these laboratory measurements validate the field measurements of Pakala et al. For the two specimens, it aims to link the noise characteristics (magnitude and frequency) with the specimen parameters (power supply and geometric dimensions) to predict the environments where wireless communications can be troublesome. By using different sets of this measured noise, we show that the statistical model of Middleton Class A can be used to model the impulsive noise in high-voltage substations better than the Gaussian model. We consider a cooperative multiple-input-multiple-output (MIMO) system to achieve the wireless sensor communication. This system uses recent MIMO techniques based on precoding like max-d min and P-OSM precoders. The MIMO precoder-based cooperative system is a potential candidate for energy saving in WSN since energy efficiency optimization is a very important critical issue. Since MIMO precoders are with Gaussian noise assumption, we evaluate the performance of several MIMO precoders in the presence of impulsive noise using estimated parameters from the measured noise.

A novel fuzzy system for edge detection in noisy image using bacterial foraging

Bio-inspired edge detection using fuzzy logic has achieved great attention in the recent years. The bacterial foraging (BF) algorithm, introduced in Passino (IEEE Control Syst Mag 22(3):52---67, 2002) is one of the powerful bio-inspired optimization algorithms. It attempts to imitate a single bacterium or groups of E. Coli bacteria. In BF algorithm, a set of bacteria forages towards a nutrient rich medium to get more nutrients. A new edge detection technique is proposed to deal with the noisy image using fuzzy derivative and bacterial foraging algorithm. The bacteria detect edge pixels as well as noisy pixels in its path during the foraging. The new fuzzy inference rules are devised and the direction of movement of each bacterium is found using these rules. During the foraging if a bacterium encounters a noisy pixel, it first removes the noisy pixel using an adaptive fuzzy switching median filter in Toh and Isa (IEEE Signal Process Lett 17(3):281---284, 2010). If the bacterium does not encounter any noisy pixel then it searches only the edge pixel in the image and draws the edge map. This approach can detect the edges in an image in the presence of impulse noise up to 30%.

Calibration details for the impulse peak insertion loss measurement

The American National Standard ANSI S12.42-2010 specifies the measurement of hearing protector performance in the presence of impulse noise. A series of calibration impulses are recorded from an acoustic test fixture (ATF) and a field microphone for peak sound pressure levels of 130, 150, and 170 dB. The averaged acoustic transfer function between the ATF and field microphone is calculated as follows: H¯ATF¯−FF¯(f)=〈FFT¯(PATF,i(t))/FFT(PFF,i(t))〉.¯ The transfer function is computed for each of the ranges of impulse levels and is applied to the field microphone measurements to estimate the unoccluded fixture levels of the ATF when hearing protection is being tested. This method allows a comparison between occluded and unoccluded waveforms. The calibration transfer function is affected by the time-alignment of the field impulse peaks, time-windowing of the impulses, and compensation for any dc bias. Time-alignment significantly affected the accuracy of predicting individual calibration levels with H¯ATF¯−FF¯. The prediction error variance was less at 170 dB than at 130 dB impulses. The time-window was varied from 2.5 to 100 ms preceding the peak of the field impulse. [Portions of this work were supported by the U.S. EPA Interagency Agreement DW75921973-01-0.]

Estimation of Impulsive Noise in an Electricity Substation

Measurements of impulsive noise in a 400/275/132-kV electricity substation are presented. The measurements are made with three antennas having overlapping bands covering the range 100 MHz-6 GHz. This range includes those bands relevant to modern wireless local area network and wireless personal area network technologies (e.g., IEEE 802.11a/b/g and IEEE 802.15.1/4), which, if proved to be sufficiently robust in the presence of impulsive noise, could play a useful role in substation monitoring and control. Impulsive events are extracted from the measured data using the wavelet packet transform, and the statistical distributions of pulse rate, pulse amplitude, pulse duration, and pulse rise time are presented. An unexpected quasi-periodic component of the noise is observed.

Robust spatial time-frequency distribution matrix estimation with application to direction-of-arrival estimation

We consider the problem of estimating spatial time-frequency distribution (STFD) matrices in the presence of impulsive noise. STFD matrices are widely used in sensor array processing for direction-of-arrival estimation and blind source separation of non-stationary sources. Conventional methods fail when the noise is non-Gaussian or impulsive. We propose robust techniques for STFD estimation which are based on pre-processing, robust position based estimation and robust covariance based estimation. The proposed methods are compared in terms of direction-of-arrival estimation performance.

Target Detection in Continuous-Wave Noise Radar in the Presence of Impulsive Noise

In the paper a problem of target detection in continuous-wave noise radar in the presence of impulsive external noise is addressed. Noise radar uses random or pseudo-random waveform as a sounding signal. Classical correlation receiver used in noise radar is optimal for external noise with Gaussian distribution. If the external noise has distribution different than Gaussian, for example impulsive noise, the performance of the detection is degraded. In order to restore the sensitivity lost due to the impulsive noise, a robustification method is proposed. In the method, a nonlinear function is applied to the signal in order to remove the outliers. The method is verified on real-life signals.

Time-domain equalizer for multicarrier systems in impulsive noise

In multicarrier communication systems, a time-domain equalizer (TEQ) can be applied to shorten the channel impulse response and to eliminate inter-symbol interference (ISI). However, the presence of impulsive noise in the channel may paralyze the operation of TEQs and subsequently lead to poor error performance. In this paper, a multicarrier receiver that incorporates a constant false alarm rate algorithm and an iterative estimation technique (CFAR-IET) in conjunction with a TEQ is proposed to increase the robustness of the receiver against impulsive noise. Furthermore, an improved version of the CFAR-IET-TEQ scheme, which uses the buffering, sorting, removing and amplitude averaging (BSRA) processes, is presented. Performance comparisons of the proposed schemes with the existing Gaussian-optimized schemes are made. Simulation results show that the BSRA-IET-TEQ scheme is an effective approach to reduce symbol error rate (SER) in impulsive channels while performing satisfactorily in Gaussian channels. Copyright © 2011 John Wiley & Sons, Ltd.

Robust multi-user detection based on quantum bee colony optimisation

To resolve local convergent difficulty of combinatorial optimisation algorithm, a quantum bee colony optimisation (QBCO) that employs novel evolutionary quantum equations is proposed. The proposed QBCO algorithm applies quantum coding and quantum rotation mechanism to evolutionary process of bee colony, which is a simple and effective discrete optimisation algorithm. Then, the proposed quantum bee colony optimisation algorithm is used to solve robust multi-user detection problem of code division multiple access (CDMA) system in the presence of impulsive noise. Furthermore, by hybridising the stochastic Hopfield neural network and quantum bee colony optimisation, the quantum state and measure state of the quantum bee are co-evolutionary in design of robust multi-user detection. The new multi-user detection algorithm can search global optimal solution in faster convergence rate. Simulation results for CDMA system are provided to show that the designed robust detectors are superior to some previous detectors in bit error rate (BER), multiple access interference and near-far resistance.

Robust Sampling and Reconstruction Methods for Sparse Signals in the Presence of Impulsive Noise

Recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. Since noise is always present in practical data acquisition systems, sensing, and reconstruction methods are developed assuming a Gaussian (light-tailed) model for the corrupting noise. However, when the underlying signal and/or the measurements are corrupted by impulsive noise, commonly employed linear sampling operators, coupled with current reconstruction algorithms, fail to recover a close approximation of the signal. In this paper, we propose robust methods for sampling and reconstructing sparse signals in the presence of impulsive noise. To solve the problem of impulsive noise embedded in the underlying signal prior the measurement process, we propose a robust nonlinear measurement operator based on the weighed myriad estimator. In addition, we introduce a geometric optimization problem based on L 1 minimization employing a Lorentzian norm constraint on the residual error to recover sparse signals from noisy measurements. Analysis of the proposed methods show that in impulsive environments when the noise posses infinite variance we have a finite reconstruction error and furthermore these methods yield successful reconstruction of the desired signal. Simulations demonstrate that the proposed methods significantly outperform commonly employed compressed sensing sampling and reconstruction techniques in impulsive environments, while providing comparable performance in less demanding, light-tailed environments.

The Performance Analysis of Error Saturation Nonlinearity LMS in Impulsive Noise based on Weighted-Energy Conservation

This paper introduces a new approach for the performance analysis of adaptive filter with error saturation nonlinearity in the presence of impulsive noise. The performance analysis of adaptive filters includes both transient analysis which shows that how fast a filter learns and the steady-state analysis gives how well a filter learns. The recursive expressions for mean-square deviation(MSD) and excess mean-square error(EMSE) are derived based on weighted energy conservation arguments which provide the transient behavior of the adaptive algorithm. The steady-state analysis for co-related input regressor data is analyzed, so this approach leads to a new performance results without restricting the input regression data to be white. Keywords—Error saturation nonlinearity, transient analysis, impulsive noise.

Mitigation of impulsive noise in digital video broadcasting terrestrial using orthogonal polarization reception

A novel method for removing impulsive noise from Digital Video Broadcasting Terrestrial (DVB-T) signals is presented in this paper. This method is based on earlier research that demonstrates the existence of correlation between the horizontal and vertical polarized electric field components of the impulsive noise. That makes it possible to predict the presence of impulsive noise in one polarization component by monitoring the amplitude level in the other one. The performance of the method is demonstrated by carrying out some simulations on a DVB-T system. Results show that the impulsive noise effect over the bit error rate can be almost wholly removed, even with high power noise pulses surging.