Presence Of Impulsive Noise Research Articles

Blind Equalization for QAM System in the Presence of Impulsive Noise

Blind Equalization for QAM System in the Presence of Impulsive Noise

Application of a surrogate model for condition monitoring of a digital twin gas turbine

Condition monitoring technology plays a crucial role in ensuring the reliable operation of gas turbines. Digital twin has propelled condition monitoring research into a new phase. This paper established a surrogate model of gas turbines for condition monitoring based on Markov-projection approximation subspace tracking. Furthermore, it explores the application of surrogate model in developing digital twin for gas turbines. The study initially establishes a Markov matrix and acquires an observation vector, utilizing the framework of the linear model. Utilizing real-time measurement data of gas turbine, the signal subspace of the observation vector autocorrelation matrix is updated through the projection approximation subspace tracking. By aligning this signal subspace with the generalized observability matrix, the identification results of the surrogate model parameters are obtained online. Furthermore, a variable weight projection approximation subspace tracking method has been proposed to enhance the algorithm robustness. Simulation and real experiment demonstrate that the surrogate model output effectively tracks the real-time changes in gas turbine measurement data. When faults and degradation arise, condition monitoring can be achieved by analyzing the evolution of model parameters to obtain feedback information from the gas turbine. The proposed method maintains its robustness in the presence of impulsive noise. These features offer a novel approach for the development of gas turbine digital twin.

Multiply Complementary Priors for Image Compressive Sensing Reconstruction in Impulsive Noise

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this article, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel truncated-Cauchy loss function as the metric of residual error to elevate the reconstruction robustness. Specifically, we design a complementary priors model to incorporate nonconvex nonlocal low-rank prior and deep denoiser prior for high-accuracy image reconstruction. By means of the half-quadratic optimization theory and generalized soft-thresholding technique, we also develop an alternative optimization algorithm for solving the induced nonconvex optimization problem. Numerical simulations demonstrate the robustness and accuracy of the proposed robust CS method compared to some recent CS methods for image reconstruction in impulsive noise.

Evaluation of a multi-function in-ear device performance in the presence of impulse noise using acoustic test fixtures

Real-time noise exposure monitoring has the potential to assess risk before damage occurs. We present on the development of an in- ear device that (1) provides hearing protection; (2) measures the noise in the ear canal to estimate exposure dose; and (3) monitors distortion product otoacoustic emissions (DPOAE) over time. To validate the device’s ability to measure impulse noise under hearing protection, we measured the response in a field test. A single AR15 was shot multiple times, and four prototypes were used in multiple acoustic test fixtures (ATFs) at different relative distances from the exit of the gun. The ATFs included a G.R.A.S 45 CB in addition to a head simulator designed to detect both air- and bone-conducted sound. We evaluated the impulse peak insertion loss (IPIL) metric of the device inserted by itself or in combination with other hearing protection (“double protection” condition) to assess attenuation of impulsive noises. We also compared the device's microphone measurements to those obtained through the ATF microphones, and to the recordings of external microphones located at or near the ear of each ATF. Results demonstrate the value of testing in-ear devices in acoustic test fixtures during development phases.

An Enhanced Multi-target Multi-Bernoulli Particle Filtering for Direction of Arrival Tracking in the Presence of Impulsive Noise

An Enhanced Multi-target Multi-Bernoulli Particle Filtering for Direction of Arrival Tracking in the Presence of Impulsive Noise

Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms

Mitigating low-frequency noise in various industrial applications often involves the use of the filter-x least mean squares (FxLMS) algorithm, which relies on the mean square error criterion. This algorithm has demonstrated effectiveness in reducing noise induced by Gaussian noise within noise control systems. However, the performance of this algorithm experiences significant degradation and does not converge properly in the presence of impulsive noise. Consequently, to uphold the stability of the ANC system, several robust adaptive algorithms tailored to handle shock noise interference have been introduced. This paper systematically organizes and classifies robust adaptive algorithms designed for impulse noise based on algorithmic criteria, offering valuable insights for the research and application of pertinent active impact noise control methods.

Power quality enhancement in the presence of impulsive noise through novel control

Power quality enhancement in the presence of impulsive noise through novel control

Parameter Estimation of LFM Signals Based on FOTD-CFRFT under Impulsive Noise

Due to the short duration and high amplitude characteristics of impulsive noise, these parameter estimation methods based on Gaussian assumptions are ineffective in the presence of impulsive noise. To address this issue, a LFM signal parameter estimation method is proposed based on FOTD and CFRFT. Firstly, the mathematical expression of FOTD is presented and its tracking performance is verified. Secondly, the tracked signal is subjected to discrete time CFRFT, and a mathematical optimization model for LFM signal parameter estimation is established on the fractional spectrum characteristic. Finally, a correction method for non-standard SαS distributed noise is proposed, and the performance of parameter estimation under both standard and non-standard SαS distributions are analyzed. The simulation results show that this method not only effectively suppresses the impact of impulsive noise on the fractional spectrum of LFM signal, but also has better parameter estimation accuracy and stability in the low GSNR. The proposed method is particularly effective under the measured noise environment, as it successfully suppresses the impact of impulsive noise and achieves high-precision parameter estimation.

Direction of Arrival Estimation with Nested Arrays in Presence of Impulsive Noise: A Correlation Entropy-Based Infinite Norm Strategy

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), wherein the performance of traditional subspace-based DOA estimation algorithms deteriorates significantly. In this paper, we propose a correlation entropy-based infinite norm preprocessing algorithm, which can be applicable to any type of impulsive noise. Each snapshot of the sensor array data is processed by an exponential kernel function with the infinite norm, which can effectively combat the outliers. Furthermore, we construct the equivalent second-order covariance matrix and perform DOA estimation using classical subspace methods. Simulation results demonstrate the effectiveness of the proposed method for both symmetric α-stable distribution and the Gaussian mixture model.

Performance of polar codes with low complexity cyclic redundancy check aided bit‐flipping for successive cancellation list decoding over power line channels

SummaryPower lines have complex transmission characteristics and noise interference when used as information transmission channel, thus limiting their data‐carrying rate and reliability. Impulsive noise is the most dominant factor degrading the performance of communication systems over power lines. To combat the impulsive noise, polar codes are introduced into the orthogonal frequency division multiplexing (OFDM)‐based power line communication (PLC) system. Because the successive cancelation list bit‐flip (SCLF) decoding algorithm of polar codes has high decoding complexity, a low complexity segmented cyclic redundancy check (CRC)‐aided SCLF decoding is proposed to improve the decoding performance. The decoding process can be terminated early with segmented CRC, which can reduce the bit‐flip decoding complexity significantly with short and medium block lengths. Experimental results demonstrate that for various configurations, the proposed decoder outperforms the traditional CRC‐aided SCL decoder in terms of decoding performance and complexity, and the suggested polar codes‐based OFDM‐based PLC system can reduce the bit error rate of the system in the presence of impulsive noise.

Sliding-Window TD-FrFT Algorithm for High-Precision Ranging of LFM Signals in the Presence of Impulse Noise

To address the performance degradation of the conventional linear frequency modulation signal ranging method in the presence of impulse noise, this paper proposes a novel technique that integrates a sliding-window tracking differentiator (TD) with the fractional Fourier transform (FrFT) ranging method. First, the sliding-window TD filtering algorithm is used to suppress the noise in the echo. Subsequently, the filtered signal is subjected to FrFT to calculate the time delay based on the difference in the peak point positions in the fractional domain for realizing target ranging. The simulation results show that the proposed method can effectively suppress impulse noise of different intensities and achieve an accurate and robust ranging of the target.

RIS-Aided Wireless Sensor Network in the Presence of Impulsive Noise and Interferers for Smart-Grid Communications

RIS-Aided Wireless Sensor Network in the Presence of Impulsive Noise and Interferers for Smart-Grid Communications

Curvelet based robust improved sine adaptive filter for feedback cancellation in hearing aids

Acoustic feedback is a common and persistent issue in hearing aids, which can lead to limitations in the achievable amplification and significant degradation in sound quality, including howling artefacts. Recently, the maximum versoria criteria (MVC) algorithm has been developed to reduce the acoustic feedback in hearing aids in the presence of impulsive noise. However, the robust technique MVC has not addressed the feedback signal’s sparseness characteristic while developing an adaptive feedback canceller. This paper proposes a robust and sparsity-aware technique called curvelet-improved sine-based adaptive filtering (CISAF) algorithm for adaptive feedback cancellation (AFC) in hearing aids using the prediction error method (PEM) in the case of non-gaussian interference. The step factor is enhanced based on the logarithmic cosh function to speed up the algorithm’s convergence while guaranteeing a low steady-state error. This variable step size (VSS) is included with CISAF as CISAF-VSS technique. Computer simulations show that the suggested method achieves a good convergence rate, maintains high speech quality, keeps a high segmental SNR (segSNR) and short-time objective intelligibility (STOI) at steady-state.

DOA Estimation With Co-Prime Array by a Combined BNC-PFLOM Method in Presence of Impulsive Noise

Direction of arrival (DOA) estimation with co-prime array is well studied in the field of array signal processing, but most research assumes noise to be Gaussian white noise. In practical scenarios, there might be impulsive noise, which has neither second-order statistics nor high-order cumulant, therefore cannot be processed directly in co-prime array configurations. In addition, the signals received by the co-prime virtual array are of single snapshot, which can be considered as coherent virtual signals coming from the same source and therefore performance degradation in DOA estimation. To solve the above mentioned problems, a combined bounded non-linear covariance (BNC) and phase fractional low-order moments (PFLOM), called BNCPFLOM, is proposed in this paper. The proposed BNC-PFLOM combines the merits of both BNC and PFLOM by constructing equivalent data covariance matrices of the received signal to lower the impact of impulsive noise. Moreover, the proposed BNC-PFLOM can build multiple pseudo snapshots of virtual signals from the co-prime array by using different orders of the proposed method. With the increase of pseudo snapshots, the proposed method is more robust in DOA estimation. Besides, the pseudo snapshots are developed from different methods, which implies different virtual signals coming from different sources. Therefore, the correlation between the virtual co-array signals can be reduced; and the proposed method direct estimates the DOAs of signals without spatial smoothing when the number of incoming signals is small. The proposed method generates multiple pseudo snapshots, which improves the resolution and accuracy in DOA estimation with co-prime array, and can be used for radar, sonar, communication systems, etc. Simulation results demonstrate the effectiveness of the proposed method, which outperforms the conventional BNC, FLOM, and PFLOM with smaller root means square error (RMSE) in terms of the generalized signal to noise ratio (GSNR) and factor of impulsive noise.

Acquisition of Visible Satellites Based on Antenna Arrays via Joint $\ell _{1,1}$-Norms Minimization

This work tackles the acquisition of visible satellites based on an antenna array in the presence of impulsive noise. First, the joint angle and number estimation of visible satellites is formulated as a joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1,1}$</tex-math></inline-formula> -norms minimization problem by exploiting the spatial sparsity of visible satellites. Then, in order to obtain a closed-form solution, a generalized conjugate function for complex-valued matrix variable is introduced and the joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1,1}$</tex-math></inline-formula> -norms minimization is transformed into a Lagrangian dual function maximization. Furthermore, the dual ascent framework and subgradient technique are employed to obtain a suitable iterative solution, where the convergence rate and steady-state value can be flexibly adjusted. Numerical results are presented to demonstrate the effectiveness of the proposed approach.

A robust iterative receiver for single carrier underwater acoustic communications under impulsive noise

Achieving reliable underwater acoustic (UWA) communications is challenging due to the rapid changes of UWA channels and presence of impulsive noise. The soft decision feedback equalization (SDFE) has attracted much attention due to its capability of tackling the rapid changes of UWA channels. However, the existing SDFE exhibits error floor and even divergence due to the presence of severe impulsive noise. To overcome this problem, an improved SDFE (ISDFE) is proposed, which incorporates impulsive noise detection, clipping level estimation and damping to handle the impulsive noise. As the performance of the ISDFE also depends on the accuracy of channel estimation (CE), we propose a variable kernel width maximum complex correntropy criterion Kalman filter (VKW-MCCC-KF) algorithm to achieve robust and accurate CE under impulsive noise. With the proposed ISDFE and VKW-MCCC-KF, an iterative receiver for single carrier (SC) UWA mobile communication under impulsive noise is proposed, which incorporates the adaptive broadband Doppler compensation, VKW-MCCC-KF based CE and ISDFE, working in an iterative manner. Experimental results show that, compared with existing receivers, the proposed receiver achieves better performance and robustness to impulsive noise under fast changing UWA channels.

Nonlocal low-rank plus deep denoising prior for robust image compressed sensing reconstruction

It is challenging for current compressive sensing (CS) approaches to reconstruct image from compressed observations with impulsive noise and outliers, termed robust image CS problem. In this paper, we propose a novel reconstruction model for the robust image CS reconstruction problem in the presence of impulsive noise. To ensure high-quality image reconstruction, we develop a nonlocal low-rank plus deep denoising prior model to simultaneously capture the nonlocal self-similarity (NSS) and deep prior, leading to a complementary reconstruction effect. Moreover, the robust M-estimator is utilized to suppress the outliers, which can strongly improve the robustness to impulsive noise. Considering the nonconvexity and complexity, both the half-quadratic (HQ) strategy and the alternating minimization method are employed to minimize the resulting large-scale optimization problem. Extensive experiments have demonstrated the effectiveness and superiority of our method significantly than the existing state-of-the art CS methods in terms of both visual quality and quantitative indexes under impulsive noise.

Robust sparse Bayesian learning based on the Bernoulli-Gaussian model of impulsive noise

Traditional sparse reconstruction methods suffer from performance degradation in the presence of impulsive noise. In this paper, we adopt the Bernoulli-Gaussian distribution to model the impulsive noise and introduce the hidden state variables to indicate the state of the noise. Based on the new hierarchical Bayesian model, a robust sparse reconstruction algorithm called Bernoulli-Gaussian robust sparse Bayesian learning (BG-RSBL) is developed through the variational Bayesian inference framework. Different from existing robust reconstruction methods, BG-RSBL does not make sparse assumptions about the impulses and outliers. The employment of the Bernoulli-Gaussian model enables BG-RSBL to adapt to environments with a wider range of impulse densities. Comparative analysis shows that the reconstruction performance can be improved by including the outliers in posterior inference with appropriate weightings. Simulations performed under both Bernoulli-Gaussian and symmetric α-stable distributed impulsive noise demonstrate the robust and outstanding reconstruction performance of the proposed algorithm.

An Efficient Filtering Algorithm against Impulse Noise in Communication Systems

&lt;p&gt;The kernel adaptive filter (KAF), which processes data in the reproducing kernel Hilbert space (RKHS), can improve the performance of conventional adaptive filters in nonlinear systems. However, the presence of impulse noise can seriously degrade the performance of KAF. In this paper, we propose a kernel modified-sign least-mean-square algorithm (KMSLMS) to mitigate the impact of impulse noise in communication systems. Moreover, we apply the nearest-instance-centroid estimation (NICE) algorithm to reduce the computational complexity of our KMSLMS algorithm, called the NICE-KMSLMS algorithm. Finally, computer simulations were used to evaluate the effectiveness of our proposed method. Compared with the conventional kernel least-mean-square algorithm (KLMS), our proposed method can improve the testing mean-squared error (MSE) by 2.32 dB and 7.39 dB for the nonlinear channel equalization and Mackey-Glass chaotic time series prediction problems, respectively. Furthermore, the testing MSE degradation caused by combining the NICE algorithm with our KMSLMS algorithm is negligible but can save about 55% computational cost in terms of the required mean size.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

General Robust Proportionate NSAF Algorithm With a Step-Size Converter

To identify sparse systems in the presence of impulsive noises, we propose a general robust proportionate normalized subband adaptive filtering (R-PNSAF) algorithm. Furthermore, to achieve fast convergence and low steady-state misadjustment, we develop a step-size converter (SSC) for R-PNSAF which results in the SSC-R-PNSAF algorithm, which selects the optimal step-size by comparing the mean square deviation at each iteration of the algorithm under given different step-sizes. Simulation results demonstrate the superiority of the proposed scheme in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -stable noise scenario over the competing techniques.