Impulsive Interference Research Articles

A Semiblind Receiver for a Two-Way UAV-Aided PIC System Based on the PARAFAC Model

In this paper, we consider a two-way communication system using the unmanned aerial vehicle (UAV) as a relay (UAV-aided). This system eliminates impulse interference through an adaptive filter based on the least mean square (LMS) and uses the received signal transmitted by the UAVs to construct a parallel factor (PARAFAC) model. Based on the identifiability condition of the PARAFAC model, a pulse interference cancellation orthogonal pilot tensor (PIC-OPT) receiver without iteration is proposed. Our algorithm is also used in millimeter-wave to achieve the acquisition of channel information. Compared with the least squares method, the simulation results demonstrate the superiority of the proposed semiblind receiver in terms of the relative mean square error and bit error rate.

A Frequency Measurement Method for Protection of Smart Circuit-breaker

The application scale of new energy such as wind energy and solar energy in China is increasingly large. One of the characteristics of these energy sources is instability. Circuit-breakers play an important role protection in distributing these energy sources. Aiming at a new type of smart circuit-breaker architecture, this paper proposes a frequency measurement method for protection function, that is, the waveform sample value of high precision and high sample rate output by the DMA of the metering chip. The FIR filter is used to eliminate harmonic interference, impulse interference, etc of sample value. The frequency value is calculated by the interval duration between the two cross-zero points of sample value, and the simulation process, as well as the results, are given.

Short-Packet Interleaver Against Impulse Interference in Practical Industrial Environments

Impulse interference is an important cause of transmission failure in the industry environments targeted by the Wireless High Performance (WirelessHP). As interleavers are commonly used to improve the reliability on the Orthogonal Frequency Division Multiplexing (OFDM) symbol level for long packet transmission, this paper considers the feasibility of applying short-packet bit interleaving to enhance the impulse/burst interference resisting capability on both OFDM symbol and frame level. Using the Universal Software Radio Peripherals (USRP) and PC hardware platform, the Packet Error Rate (PER) performance of interleaved coded short-packet transmission with Convolutional Codes (CC), Reed-Solomon (RS) codes, and RS+CC concatenated codes are tested and analyzed. The IEEE 1613 standard is applied for impulse interference generation, and extensive PER tests of CC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1/2)$ </tex-math></inline-formula> and RS <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(31,21)+$ </tex-math></inline-formula> CC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(1/2)$ </tex-math></inline-formula> concatenated codes are conducted. We prove the effectiveness of bit interleaved coded short-packet transmission in real factory environments with practical experiments. Moreover, we investigate how PER performance depends on the interleavers, codes and impulse interference power and frequency.

An Improved Time-Varying Morphological Filtering and Its Application to Bearing Fault Diagnosis

Morphological filtering (MF) is a typical fault feature extraction technology, which has been widely used in rolling bearing fault diagnostics. The structural element (SE) length has an important influence on feature extraction and noise removal, and the filtering result obtained by the filtered signal under a single SE length or the weighted average of different filtered signals under partial SE lengths is limited and insufficient. To address this problem, a transient feature extraction method called improved time-varying morphological filtering (ITVMF) is proposed for bearing fault diagnosis, wherein the SE length is adaptively determined according to the inherent characteristics of vibration signals. Moreover, the autocorrelation envelop spectrum (AES) is employed to further eliminate fault-unrelated components. The main innovation of this method is to automatically extract bearing fault-related transient features using a novel time-varying SE (TVSE) based on autocorrelation while effectively eliminating the interference of random impulses and strong background noise on bearing fault features. The developed ITVMF-AES is investigated and validated on the simulation signals, accelerated bearing degradation data, and artificial experimental test data, and compared with three existing feature extraction methods. The results demonstrate that the developed methodology is more efficient in excavating transient impulse features and identifying bearing faults than the comparison methods, and has potential engineering application value.

DOA Tracking With Nested Arrays via an Improved Multisource Multi-Bernoulli Filtering in the Presence of Impulsive Noise

Traditional DOA tracking algorithms have poor performance when considering source time-varying and impulsive noise interference. To address these issues, an improved multisource multi-Bernoulli (IMSMB) particle filtering algorithm based on phased fractional lower order moment is proposed. In the IMSMB algorithm, an unscented transform method is used to operate the surviving particles, which increases the diversity of resampling particles. Moreover, the proposed method is extended to nested array to improve tracking accuracy. Simulation results verify the superiority of the proposed method compared to the existing DOA tracking algorithm.

A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

Recently, the sparsity-aware sign subband adaptive filter algorithm with individual-weighting-factors (S-IWF-SSAF) was devised. To accomplish performance enhancement, the variable parameter S-IWF-SSAF (VP-S-IWF-SSAF) algorithm was developed through optimizing the step-size and penalty factor, respectively. Different from the optimization scheme, we devise a family of variable step-size strategy S-IWF-SSAF (VSS-S-IWF-SSAF) algorithms based on the transient model of algorithms via minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. And in order to enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. It is worth mentioning that the presented algorithms could acquire lower computational requirements and exhibit higher steady-state estimation accuracy obviously and acceptable tracking characteristic in comparison to the VP-S-IWF-SSAF algorithm. In addition, the stable step-size range in the mean and mean square sense and steady-state performance are concluded. And the computational requirements are exhibited as well. Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to other related algorithms within various system inputs under impulsive interference environments.

Modified Huber M-Estimate Function-Based Distributed Constrained Adaptive Filtering Algorithm Over Sensor Network

Most of the existing distributed adaptive filtering algorithms over wireless sensor networks (WSNs) are developed, aiming to solve unconstrained network optimization problems. However, in practice, the weight coefficients of the filter may need to satisfy a set of linear equations. Thus, a distributed adaptive algorithm that can solve the sensor network optimization problem under constraints is needed. Considering the possible impulsive interference in the observed signals, a novel robust distributed constrained adaptive algorithm called diffusion constrained least mean M-estimate (D-CLMM) is proposed by using the modified Huber function (MHF), which endows the network robustness to impulsive noise. The transient, steady-state performances and stability of the proposed D-CLMM are studied with the aid of some commonly used assumptions and verified by computer simulations. Moreover, the effectiveness of D-CLMM is verified in distributed parameter estimation and beamforming applications in non-Gaussian noise environments.

The nuclear safety monitoring system for fuel-containing materials located in destroyed unit No. 4 of the Chornobyl NPP and proposals for its modernization

The paper presents a brief description of the Nuclear Safety Monitoring System (NSMS), which is a part of the Integrated Automated Monitoring System of the "Shelter" object (a facility that covers the destroyed Unit No. 4 of the Chornobyl NPP). Further development of the NSMS is proposed by introducing algorithms for automatic identification of neutron anomalies with a help of fuzzy logic and statistical methods; digital filtering of specific irregular impulse interferences leading to metrological system failures; algorithms for predicting changes in neutron flux density and assessing changes in the nuclear hazard of fuel containing materials accumulations.

Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method.

In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.

Re-weighted zero attracting adaptive exponential FLAF with maximum correntropy criterion for robust sparse nonlinear system identification

The profoundly employed linear-in-the-parameter nonlinear adaptive filtering techniques of functional link adaptive filter (FLAF) and adaptive exponential FLAF (AEFLAF) often exhibit deteriorated performances in the wake of varying sparsity conditions and impulsive noise interferences. In this regard, a robust nonlinear adaptive filtering technique is introduced leveraging the benefits of nonlinear similarity index maximum correntropy criterion (MCC). Additionally a log sum penalty function is included in the derived cost function in response for sparse system identification. The entire robust nonlinear adaptive filter in this paper is constructed upon the framework of AEFLAF based on affine projection algorithm (APA) as MCC based reweighted zero attracting AEFLAF (MR-AEFLAF). Besides, steady state analysis of the proposed MR-AEFLAF is carried out establishing the condition for stability. Simulations with white, colored noise and speech segment illustrate the efficacy of the proposed technique for robust nonlinear adaptive filtering in system identification and acoustic feedback cancellation in hearing aids scenario. The performance of the designed adaptive filter is assessed quantitatively and qualitatively both.

Mean-square stabilization of impulsive neural networks with mixed delays by non-fragile feedback involving random uncertainties

In this paper, we consider a class of neural networks with mixed delays and impulsive interferences. Firstly, a sufficient condition is given to ensure the existence and uniqueness of the equilibrium point of the proposed neural networks by employing the contraction mapping theorem. Secondly, we discuss the issue of the exponential stability in mean-square of the equilibrium point by a non-fragilely delayed output coupling feedback which involves stochastically occurring gain oscillations. The designed feedback input can be tolerant of limited stochastic fluctuations of control gains and be robust against potential errors caused by factors like round-off. By combining methods of Lyapunov–Krasovskii functional and free-weighting matrix, a delay-dependent output coupling feedback with stochastically occurring uncertainties is designed and linear-matrix-inequalities(LMIs)-based sufficient conditions for the exponential stabilization in mean square are derived. Finally, three numerical examples are presented to illustrate the feasibility of theoretical results with a benchmark real-world problem.

Local lowest-rank dynamic mode decomposition for transient feature extraction of rolling bearings

The fault diagnosis mainly relies on the detection of periodic pulse components caused by the local damage. However, the impulsive component in bearing vibration signals is usually disturbed by noise and some harmonics due to the harsh working environment, which makes great challenge to fault diagnosis. To enhance the performance in fault diagnosis of rolling bearing, a local lowest-rank dynamic mode decomposition (LLRDMD) is proposed in this paper. Firstly, an improved projection operator is proposed to reduce the noise of augmented snapshot matrix and thus eliminating the bias of high-dimension dynamic system matrix. Secondly, by solving a local lowest-rank optimization problem, the impulsive component and interference components in the high-dimension dynamic system matrix are separated. Compared with DMD, the proposed method achieves better performance in the extraction of transient pulse component while avoiding the problems of parameter selection and mode selection. Lastly, the proposed method is applied to three sets of real datasets. Compared with existing methods, LLRDMD achieves a higher accuracy in fault diagnosis.

Bearing failure impulse enhancement method using multiple resonance band centre positioning and envelope integration

Due to the complex structure of electromechanical systems, impulse components with a transient duration will excite multi-resonance phenomena and generate diverse peak-like bands in the spectra of vibration signals. Resonance bands excited by bearing failure-induced impulses that characterise failure area and form, are confused with frequency components caused by impulsive interference and background noise. Given the imperceptibility of the failure features of on-machine bearings, a better identification strategy is to extract the information of multiple predominate resonance bands excited by failure, referred to as effective resonance frequency bands (ERFBs), and to then integrate them, instead of locating only the optimal resonance frequency band (ORFB). In this paper, a multiple resonance band centre positioning and envelope integration method is proposed to enhance the bearing failure features. To this end, a robust indicator, ratio of cyclic content (RCC), is utilised to construct an Improved-Protrugram to position the centres of ERFBs correctly. Then, a dichotomy is introduced to establish an iterative search algorithm for improving position efficiency. Further, the positioned sub-band components are filtered and demodulated to obtain the envelopes of failure-induced multiple resonances. Finally, a variable weighting envelop integration strategy is proposed to eliminate interference that does not coexist between different sub-band components. The effectiveness of the proposed method is verified using both simulated and experimental bearing failure signals. The results indicate that the proposed method is superior to ORFB location methods for on-machine bearing failure identification.

Convex combination of the FxAPV algorithm for active impulsive noise control

Conventional active noise control systems suffer from performance degradation when exposed to impulsive interference. Taking the maximum Versoria criterion (MVC) function as the cost function, an MVC-based filtered-x affine projection algorithm is derived by solving the inequality constraint problem. The algorithm has a fast convergence speed when dealing with active impulsive noise control. Subsequently, a convex combinatorial structure is proposed. The stability and computational complexity of the algorithms are analyzed. A genetic algorithm is used to select the appropriate parameters for different algorithms. The simulation results show that the proposed algorithm has a faster convergence rate in addition to a greater average noise reduction. Lastly, the effectiveness of the proposed algorithms is verified experimentally.

Assessment of the impact of interference signals on the decision circuit of the receiving-analyzing route of digital radio engineering systems

Currently, due to the increasing complexity of navigation support for aircraft (A/C), with the growth of requirements for them, it is increasingly necessary to develop systems for integrated processing of navigation information. The facilities for radio engineering flights support and aeronautical telecommunications are the main source of collecting processing and presenting information about the air situation to the traffic control staff. They are also used to solve the number of navigation problems. At the same time, the level of flight safety, along with the capacity of air traffic control zones depending on the decision of the ATC unit, is largely specified by the reliability of the various input information provided. At another point, most sophisticated digital tools for radio engineering flights support and aeronautical telecommunications at the output of the receiving-analyzing route have a decision or threshold circuit, the actuation of which, as a result of exposure to a mixture of noise and interference signals, reduces the reliability of information due to bursts of signal distribution, provided that impulse interference affecting the equipment and mush of receivers are not correlated. The work provides an estimate of the average amount of signal bursts at the output of the decision circuit taking into consideration the analysis of the probabilistic characteristics of the signals under consideration. For noises subject to the Rayleigh distribution and narrow-band impulse interference with a zero-mean value, the mathematical expectations of the average response time and the average number of positive bursts of the estimated process are found. The case of interfacing two airway surveillance radars “Skala-M” and “ATCR-22”, which have virtually identical operating characteristics, is analyzed. The dependence of the burst duration is calculated for the situation when the “Skala-M” radar is a source of unintended EMI for the “ATCR-22” radar.

Spectral Efficiency Evaluation of an NR-Based 5G Terrestrial Broadcast System for Fixed Reception

LTE-based 5G terrestrial broadcasting, standardized in 3GPP Release 16, allows broadcasters to reach cellular devices. In this paper, toward the development of a future terrestrial broadcast system in Release 18 or later called 5G-Advanced, a specification for New Radio (NR) based 5G terrestrial broadcasting is presented for a 6 MHz-channel raster, which is used in current digital terrestrial television broadcasting. The transmission performance is evaluated in physical layer simulations. In particular, the spectral efficiency is evaluated and compared with a second-generation DTTB system at a required carrier-to-noise ratio (CNR) of 20 dB in a fixed-reception scenario with a rooftop receiving antenna. The evaluation in an additive white Gaussian noise channel indicates that the spectral efficiency of NR-based 5G terrestrial broadcasting can be improved by 0.83 (bps/Hz) by enhancing the code length of low density parity check code, the carrier modulation scheme, guard band ratio, and cyclic prefix ratio from what available so far in Release 16. Furthermore, when frequency interleaving is incorporated, the spectral efficiency is increased by 0.75 (bps/Hz) in a short echo channel with a desired-to-undesired signal power ratio of 0 dB. The introduction of time interleaving increases spectral efficiency by 0.97 (bps/Hz) in an impulsive interference channel with a carrier-to-interference signal power ratio of -20 dB. For each interleaving, the increase in spectral efficiency tends to be higher as the required CNR increases.

Fast nonlinear Hoyergram for bearings fault diagnosis under random impact interference

Random impact interference has always been an important subject in fault diagnosis. In the fast kurtogram (FK), kurtosis is a sparsity index used to locate optimal resonance frequency bands. However, the shortcomings of the kurtosis measure are a tendency to select the random impact with a large amplitude and a relatively weak sparse measure ability, which reduces the accuracy and anti-interference of the FK method. To overcome the effects of random impact interference, this paper proposes a novel bearing fault diagnosis method named the fast nonlinear Hoyergram (FNH), in which the nonlinear Hoyer index is employed to replace kurtosis to improve the fault representation capability under random impulse interference. Firstly, Z-score normalization and a generalized nonlinear sigmoid activation function are used for signal preprocessing, and the scale distribution of the signal is changed to weaken the impact of random impact interference. Secondly, the Hoyer index, which can be viewed as the normalized form of the norm, is used to replace kurtosis to improve the sparsity measure capability. Thirdly, in the Hoyergram, the frequency band with the largest Hoyer value can be chosen as the best resonance frequency band for the squared envelope analysis. Finally, the proposed FNH is compared with the FK method through simulation and experimental signals, and the effectiveness of the proposed method is verified.

Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform for bearing fault diagnosis

The Fast Kurtogram (FK) is a widely used resonance demodulation technique for bearing fault diagnosis. In this paper, a novel method termed Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform (SRE-DTCWPT) is proposed to improve the performance of the FK from the aspects of band division and optimal band selection indicator. To obtain an excellent band division, the SRE-DTCWPT is first developed. It retains the main advantages of DTCWPT and meanwhile addresses the two key issues of frequency sub-bands disorder and frequency bands leakage. Then, a new robust evaluating indicator called reweighted kurtosis is defined. It solves the problem of kurtosis being sensitive to strong impulse interferences. Furthermore, the proposed method involves a set of envelope analysis approaches developed on different cases of fault signals to realize the enhanced identification of the bearing diagnostic information. Two simulated signals and actual bearing signals regarding different practical cases are employed to investigate the effectiveness of the proposed method. In addition, the proposed method is compared with the FK, and the results verify that the proposed method shows high potentials for extracting bearing diagnostic information from complex vibration signals.

Robust Multi-Task Diffusion Least Mean M-Estimate Adaptive Algorithm and Its Performance Analysis

Multi-task diffusion LMS (MDLMS) is an useful algorithm to infer multiple related parameter vectors concurrently through the cooperation of nodes among clusters. However, it must be pointed out that its optimal estimation can only be realized under Gaussian noise. In practical applications, this ideal noise environment is not easy to meet. For example, there will be some impulsive noise interference. At this time, it is urgent to propose a robust multi-task algorithm against impulsive noise. The modified Huber (MH) function is considered in the new robust multi-task algorithm (i.e., multi-task diffusion least mean M-estimate (MDLMM)) proposed in this brief since its simple structure and outstanding effect of restraining impulsive interference. After that, we analyze the mean and mean square performance of the MDLMM algorithm, and give the stability conditions (in the sense of mean and mean square) and the closed-form expressions of theoretical transient and steady-state network mean square derivation (NMSD) under some assumptions. Numerical simulations verify the accuracy of the theoretical analysis results and the superiority of MDLMM over other robust multi-task algorithms.

Lawson-Norm-Based Adaptive Filter for Channel Estimation and In-Car Echo Cancellation

We proposed a Lawson-norm-based adaptive estimation algorithm within the affine-project theory framework and give a name of Lawson-norm adaptive filter (LNAF) algorithm. The LNAF algorithm is derived, analyzed, and simulated for echo cancellation when background noise is impulsive, which is realized and implemented via using Lawson norm of past errors to take a sliding window on cost function to speed up convergence and achieve robustness for impulsive noises for colored signal. Simulations based on measured data are used for in-car echo cancellation and channel estimation to verify the LNAF algorithm’s performance with different inputs, which prove that the LNAF algorithm is superior to the popular AP algorithms under different impulsive interferences.