Impulsive Interference Research Articles

A robust double-channel affine projection sign algorithm for blind acoustic interferences cancellation

This brief proposes a double-channel direct affine projection sign (DC-DAPS) algorithm for blind speech enhancement applications, especially in the presence of strong acoustic noise, and intensive non-Gaussian impulsive interferences. We also derive a full analysis of the stability of the proposed DC-DAPS algorithm. The performance evaluation of the proposed algorithm confirms its accuracy even under different noisy scenarios, and demonstrates that our approach outperforms the double-channel direct affine projection (DC-DAP), and the double-channel direct normalized least mean square (DC-DNLMS) algorithms. Simulation results showed that the proposed DC-DAPS algorithm could effectively achieve improved performance in combating acoustic noise and impulsive interferences, and speeding up the convergence rate even with highly correlated input signals such as the speech signal.

Robust non-fragile hybrid control for delayed uncertain singular impulsive jump systems based on improved impulse instant-dependent auxiliary functions method

This article researches the issue of robust non-fragile hybrid control for delayed uncertain singular impulsive jump systems (USIMJSs). The key aim is to design non-fragile hybrid state feedback controllers (including a non-fragile normal state feedback controller and a non-fragile impulsive state feedback controller), which are insensitive to the uncertainties of gains of controllers and can provide sufficient tuning margins. The non-fragile normal state feedback controller can eliminate the internal impulses and overcome the external disturbances; the non-fragile impulsive state feedback controller can suppress the interference of external unstable impulses and restrain the instantaneous jumps caused by Markovian modes switching. By introducing impulse instant-dependent auxiliary functions, the improved impulse-time-dependent Lyapunov–Krasovskii functional is constructed, which can capture the information of the impulse instants and Markovian jump modes. Novel criteria of robust admissibilization for delayed USIMJSs are acquired under linear matrix inequalities framework. Lastly, the effectiveness of the derived algorithm and designed method is confirmed by simulation examples including a direct current motor-controlled inverted pendulum device and a Quarter-Car active suspension model.

Effects of asymmetric rates and impulse interference in Rock-Paper-Scissors games

The dynamics of systems composed of cyclically competing species are complicated by the fact that they are strongly influenced by fluctuations. For this reason, it is attractive to study low-dimensional cycles where these impacts are most prominent. In this paper, we investigate the impacts of asymmetric compared to symmetric reactive rates on three-species biological diversity and spatial pattern formation in a four-state case using an RPS game model on a one-dimensional lattice. In addition, we also discuss the effects of interference thresholds on biodiversity maintenance when species are impulsively disturbed under both symmetric and asymmetric rates. The results show that enhancement of the competition rate promotes biodiversity while the mobility rate has the opposite effect when these rates being symmetric. Meanwhile, changes in reproductive rates have no direct effect on biodiversity. If rates are asymmetric, only one species survives and biodiversity is negatively affected. Conversely, the outcome of single species survival is altered when species are impulsively disturbed. Under symmetric rates, an increase in interference threshold results in a shift from coexistence of three species to survival of two species. With asymmetric rates, population dynamics change from the survival of only one species to the survival of two species if the interference is introduced. Our findings may enlighten people by RPS game model to protect endangered animals, or kill off harmful species, or to engineer cyclical population to stabilize the functionality of gene circuits for cancer treatment, or to guide election campaign.

Robust Equalization for Single-Carrier Underwater Acoustic Communications Based on Parameterized Interference Model

There are several sources of artificial and naturally generated impulsive interference in marine environment which could severely degrade the acoustic communication performances. This letter tackles the equalization for single-carrier modulated systems with impulsive interference based on parameterized model, and a two-step equalization algorithm is proposed. In the first step, an iterative interference impulse estimation/cancellation can be performed and initial symbol estimation is obtained by conventional decision feedback equalization. In the second step, a joint channel, impulse, and symbol estimation for single-carrier modulation is applied to enhance the performance. The proposed algorithm is evaluated using partial-band partial-block-duration interference. Simulation results show that the proposed joint estimation algorithm has significant performance advantages over conventional algorithm in various signal-to-interference ratio levels.

A novel fault diagnosis method for bearing based on maximum average kurtosis morphological deconvolution

Abstract Maximum average kurtosis deconvolution (MAKD) effectively enhances periodic impulses in vibration signals. However, under conditions of random impulse interference, MAKD tends to amplify impulses within a single period. To address this problem, this paper proposes a maximum average kurtosis morphological deconvolution (MAKMD) method. First, on the basis of proposing a time-varying structural element more in line with the characteristics of vibration signals and constructing a new morphological gradient squared operator, an enhanced time varying morphological filtering (ETVMF) is proposed. Then, ETVMF is introduced into MAKD to eliminate the effect of random impulse. Finally, the diagonal slice spectrum (DSS) is utilized to detect the coupling frequency of the bearing, which makes the spectrum clearer and more convenient for bearing fault diagnosis. In MAKMD, the effect of random impulse is eliminated and the capability of fault feature extraction is enhanced. To demonstrate the method’s effectiveness and feasibility, experiments are conducted using simulated signals and measured bearing fault data, comparing results with existing deconvolution methods.

Automatic Identification of Space-Time Block Coding for MIMO-OFDM Systems in the Presence of Impulsive Interference

Automatic Identification of Space-Time Block Coding for MIMO-OFDM Systems in the Presence of Impulsive Interference

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.

Robust constrained cosine arctangent based adaptive filtering for feedback control in hearing aids

The striking problem of acoustic feedback remains a persistent challenge in hearing aids (HAs), imposing limitations on attainable amplification and significantly deteriorating sound quality, often manifesting as disruptive howling artifacts. With the objective of feedback mitigation, the feedback path is estimated using an adaptive filter. The constrained least mean square (CLMS) technique is commonly employed in adaptive filtering applications where it is necessary to meet specific linear constraints. However, algorithm's robustness is compromised when impulsive or non-Gaussian noise interference occurs. In lieu of this, a novel algorithm known as the robust constrained cosine arctangent (CCAT) adaptive filtering is introduced to enhance the convergence behavior. This has been formulated with the inclusion of linear constraints to the suggested cosine function integrated with the arctangent technique. This algorithm aims to address the issue of feedback cancellation in hearing aids along a set of constraints. To achieve a minimal steady-state error and expedite the convergence of the method, a novel policy that adjusts the step factor has been suggested. CCAT-VSS is a modified version of CCAT that includes a variable step size (VSS). The simulation findings demonstrate that the suggested method exhibits superior performance for misalignment, added stable gain and certain voice quality assessments.

Exponential Stability of Stochastic Time-Delay Neural Networks with Random Delayed Impulses

The mean square exponential stability of stochastic time-delay neural networks (STDNNs) with random delayed impulses (RDIs) is addressed in this paper. Focusing on the variable delays in impulses, the notion of average random delay is adopted to consider these delays as a whole, and the stability criterion of STDNNs with RDIs is developed by using stochastic analysis idea and the Lyapunov method. Taking into account the impulsive effect, interference function and stabilization function of delayed impulses are explored independently. The results demonstrate that delayed impulses with random properties take a crucial role in dynamics of STDNNs, not only making stable STDNNs unstable, but also stabilizing unstable STDNNs. Our conclusions, specifically, allow for delays in both impulsive dynamics and continuous subsystems that surpass length of impulsive interval, which alleviates certain severe limitations, such as presence of upper bound for impulsive delays or requirement that impulsive delays can only exist between two impulsive events. Finally, feasibility of the theoretical results is verified through three simulation examples.

Strategy for Detection and Suppression of Random Impulse Interferences in Gearbox Vibration Signals

Strategy for Detection and Suppression of Random Impulse Interferences in Gearbox Vibration Signals

Review of Matrix Rank Constraint Model for Impulse Interference Image Inpainting

Camera failure or loss of storage components in imaging equipment may result in the loss of important image information or random pulse noise interference. The low-rank prior is one of the most important priors in image optimization processing. This paper reviews and compares some low-rank constraint models for image matrices. Firstly, an overview of image-inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F norm is presented, and corresponding optimization iterative algorithms are provided. Then, we use different image matrix low-order constraint models to recover satellite images from three types of pulse interference and provide our experimental visual and numerical results. Finally, it can be concluded that the method based on the weighted nuclear norm can achieve the best image restoration effect. The F norm method based on matrix factorization has the shortest computational time and can be used for large-scale low-rank matrix calculations. Compared with nuclear norm-based methods, weighted nuclear norm-based methods and truncated nuclear norm-based methods can significantly improve repair performance.

Affine projection algorithms based on sigmoid cost function

In this paper, two new algorithms are proposed using modified sigmoid activation function as the cost function and combining with affine projection (AP) method, which are sigmoid affine projection sign algorithm (SAPSA) and sigmoid affine projection algorithm (SAPA). Conventional AP-type algorithms converge fast but are susceptible to impulsive interference. The sigmoid function has an upper bound, thus reducing the effect of impulse interference. The proposed algorithms combine the advantages of fast convergence speed of the affine projection method and strong noise resistance of the sigmoid cost function. Meanwhile the L2-norm constraint is used to adjust the updating formula of the weight vector, which further improves noise resistance. Several simulation experiments show that SAPSA and SAPA are superior to other AP-type algorithms under impulse interference.

Model of Satellite Communication Channel Functioning under Conditions of Episodic Synchronization with Pulse Interference Flows

The article investigates the effect of pulse interference on information reception in conditions of episodic synchronization of frames of the physical level of a satellite communication channel with streams of radio pulses of unintended interference. An analytical model of the influence of pulse interference on the reception of information in a satellite communication channel under conditions of episodic synchronization of physical-level frames with pulse interference streams is proposed. Using the example of the DVB family of standards, the combined effect of noise and unintended impulse interference on the conditional error probabilities when receiving a synchro group, the service part of the header and the information part of the frame is shown. Estimates of the average number of frames of the physical level for the duration of the interval of episodic synchronization, the number of intervals of episodic synchronization and the proportion of elementary parcels in the frame exposed to interference, depending on the duration of the pulse interference, are given. It is shown that there are such relations between the duration of the interference pulse and the continuity of the sequence, in which the phenomenon of the episodic synchronization of physical-level frames with the flow of pulse interference has a significant impact on the functioning of the satellite communication channel. The dependences of the probability of erroneous reception of a frame of the physical level of a satellite communication channel on the signal-to-interference ratio at the fixed signal-to-noise ratio and on the duration of the interference pulse are obtained. It has been found that at high signal-to-noise ratios and the duration of the interference correlated with the duration of the service part of the frame, but significantly less than the duration of the frame, the probability of erroneous reception of the frame may be higher than at lower signal-to-noise ratios due to errors when receiving the service part of the frames.

Variable-Step-Size Efficient Proportionate Affine Projection Sign Algorithms

For sparse system identification, a memory-improved proportionate affine projection sign algorithm with a simplified, generalized correntropy induced metric (SGCI-M-IPAPSA) has good filtering performance. However, the SGCI-M-IPAPSA is based on a fixed step size and is not always the best choice. To overcome the limitation of a fixed step size in filtering precision and convergence speed under non-Gaussian impulsive interferences, in this paper, we apply the combined-step-size idea and a variable-step-size method based on the mean-square deviation to the SGCI-M-IPAPSA, respectively, and propose two new robust algorithms to enhance the filtering performance of the SGCI-M-IPAPSA. In addition, by combining the combined-step-size and proposed variable-step-size methods, we propose a fresh combined variable-step-size way and apply it to the SGCI-M-IPAPSA. The convergence of the proposed algorithms is also elaborated, and a conditional decision on the mean-square error is used to cope with abrupt changes. The better performances of the proposed algorithms than the conventional SGCI-M-IPAPSA in terms of the filtering accuracy and convergence rate are demonstrated with non-Gaussian impulsive interferences for sparse system identification, abrupt changes and colored inputs.

Formula omitted]-norm minimization for outlier-resistant elliptic positioning in [formula omitted]-stable impulsive interference

This short communication considers the problem of robust elliptic positioning (EP) in α-stable impulsive interference based on the ℓp-norm minimization criterion. We start with converting the associated nonconvex optimization task into the well-established ℓp-minimization framework of iteratively reweighted least squares (IRLS). While the effect of p is taken into account by a weight update step in each IRLS iteration, we employ an assortment of surrogate functions, in order to effectively handle the location update subproblem using the majorization–minimization technique. The superiority of our proposal over a number of existing EP methods in terms of localization accuracy in α-stable impulsive noise is demonstrated via numerical simulations.

Robust equalization for single-carrier underwater acoustic communication in sparse impulsive interference environment

Impulse noise significantly distorts the background noise within communication systems, causing a notable departure from the Gaussian distribution. This distortion, in turn, exerts a detrimental influence on the performance of conventional single-carrier frequency domain equalization techniques. In this paper, we introduce a novel algorithm for joint channel, interference, and symbol estimation within single-carrier systems. This algorithm is grounded in the framework of variational Bayesian inference and possesses the unique capability of simultaneously accounting for the sparsity inherent in the channel and impulse interference. In the course of processing, it leverages multi-parameter probability distributions to effectively model the intricate posteriori probabilities associated with impulse interference vectors and channel vectors. This, in turn, facilitates the computation of maximum a posteriori estimates and subsequently enables the estimation and elimination of interference. We validate the efficacy of our proposed method through both numerical simulations and the processing of experimental data. Our results unequivocally demonstrate the robustness and manageable computational complexity of our approach within the context of impulse interference-prone environments.

An intelligent signal processing method against impulsive noise interference in AIoT

In complex industrial environments such as the Internet of Things in coal mines, large mechanical and electrical equipment can generate powerful impulsive noise, which can cause sudden errors. Because it is difficult to establish an accurate channel model, the performance of current error control techniques is limited. To enhance the reliability of information recovery in the Internet of Things in coal mines, the traditional method of shortening the communication distance between sensors is often utilized, but this can be costly. Therefore, this article proposes an intelligent signal processing method against impulsive noise interference that draws on the concept of the Artificial Intelligence of Things (AIoT) and incorporates deep learning technology. This method replaces the traditional sensor signal processing module with a Convolutional Neural Network (CNN), which learns the intricate mapping relationship between transmitted information and sensor signals in impulsive noise environments. Simulation results demonstrate that the proposed method outperforms the traditional sensor signal processing method in three impulsive noise environments by achieving a lower Bit Error Rate (BER). Moreover, this method adopts an improved lightweight neural network, which is more conducive to the deployment of mobile terminals in the Internet of Things.

Robust improved multiband-structured subband adaptive filter for active noise control with impulsive interference

The feedforward filtered-x least mean square algorithm is extensively implemented for active control of broadband noise. However, the control performance is substantially deteriorated due to colored noise and the presence of uncorrelated disturbances near the reference and error sensors. To tackle these issues, in this paper, a robust improved multiband-structured subband adaptive filter based on logarithmic and total least squares method is proposed for active control. Unlike the conventional method of total least squares, the proposed algorithm adopts logarithmic and Rayleigh quotient functions. The closed loop implementation of the improved multiband-structured subband adaptive filter is adopted to meet the delayless requirement. The proposed algorithm is well-suited for environments where the reference signal is highly correlated and the residual noise is contaminated by impulsive noise. Furthermore, an affine combination of the proposed algorithm is developed to meet the complementary requirements of faster convergence and improved noise reduction. Eventually, the stability and computational complexity are studied. Simulation results using measured acoustic paths in an anechoic chamber and a normal room illustrate the effectiveness of the proposed algorithm for controlling broadband noise with impulsive interference. In addition, the tracking control performance is evaluated in a time-varying acoustic environment.

Detection and cancellation method of impulse noise in gearbox non-stationary vibration signals—a case study of port ship unloader

This work proposes a signal preprocessing framework that cancels out impulse noise in the non-stationary gearbox vibration signals of a ship unloader, which reduce the interference of non-cyclic impulses on the damage detection. The algorithm involves three main steps: (i) Preliminary localization of impulse noise, including suppressing components related to normal machine operation, segmenting the signal, and localizing abnormal sub-segment signal based on higher-order statistical criteria. (ii) Detection of impulse boundaries. The influence of random impulse noise on the signal envelope spectrum is studied, and an impulse indicator is constructed to accurately detect the boundaries of impulse noise in abnormal sub-segment signals. (iii) Cancellation of impulse noise. Replace the identified random impulse with Gaussian white noise. Laboratory and industrial data confirm the excellent performance of the proposed algorithm. It can accurately identify and cancels out the non-cyclic impulse noise in the non-stationary gearbox vibration signal, which facilitates the subsequent damage detection.

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