Impulsive Interference Research Articles

Performance Analysis of Underwater Acoustic Communications in Barrow Strait

The Arctic Ocean and its various shallow passages are rapidly changing due to global warming. The development of a large-scale wireless network that collects and distributes oceanographic data remotely would be an extremely valuable asset to researchers. This concept, however, is completely dependent upon the ability to acoustically transmit digital information under water over long ranges, which is not currently available. In this work, we investigate the performance of four transceivers that are based on frequency-hopping binary frequency-shift keying and multilevel phase-shift keying (M-PSK) signaling in the 300 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula> 400-Hz acoustic band. The transceivers use low-rate coding that provides bit rates from 1.8 to 13.3 b/s (bits per second). Their bit error rates are computed based on real data recorded in Barrow Strait (NU). During the 2019 experimental activities, the transmitter was towed by a supporting vessel at an average speed of 4 kn. The receiver, a vertical hydrophone array, was at distance between 14 and 33 km. Additionally, the received signals experienced extended multipath propagation and strong in-band impulsive interferences. For a single-hydrophone receiver, our postprocessing analysis shows reliable bit rates up to 6.7 b/s. When processing uses the array and performs multichannel decision feedback equalization (DFE), the maximum designed rate, i.e., 13.3 b/s, is confirmed. Further analysis reveals that the experimental links could support higher data rates. Our results demonstrate that 2-PSK signaling combined with single-channel DFE and 4-PSK signaling combined with multichannel DFE could reliably achieve 100 and 200 b/s, respectively.

An optimized initialization for LDPC decoding over GF(q) in impulsive noise environments.

Modern navigation satellite communication has the characteristic of high transmitting rate. To avoid bit errors in data transmission, low density parity check (LDPC) codes are widely recognized as efficient ways for navigation communication. Conventionally, the LDPC decoding is applied for additive white Gaussian noise (AWGN) channel and degrades severely while facing the impulsive noise. However, navigation communication often suffers from impulsive interference due to the occurrence of high amplitude "spikes". At this time, the conventional Gaussian noise assumption is inadequate. The impulsive component of interference has been found to be significant which influences the reliability of transmitted information. Therefore the LDPC decoding algorithms for AWGN channel are not suitable for impulsive noise environments. Consider that LDPC codes over GF(q) perform better than binary LDPC in resisting burst errors for current navigation system, it is necessary to conduct research on LDPC codes over GF(q). In this paper, an optimized initialization by calculating posterior probabilities of received symbols is proposed for non-binary LDPC decoding on additive white Class A noise (AWAN) channel. To verify the performance of the proposed initialization, extensive experiments are performed in terms of convergence, validity, and robustness. Preliminary results demonstrate that the decoding algorithm with the optimized initialization for non-binary LDPC codes performs better than the competing methods and that of binary LDPC codes on AWAN channel.

A robust subband adaptive filter algorithm for sparse and block-sparse systems identification

This paper presents a new subband adaptive filter (SAF) algorithm for system identification scenario under impulsive interference, named generalized continuous mixed p-norm SAF (GCMPN-SAF) algorithm. The proposed algorithm uses a GCMPN cost function to combat the impulsive interference. To further accelerate the convergence rate in the sparse and the block-sparse system identification processes, the proportionate versions of the proposed algorithm, the L0-norm GCMPN-SAF(L0-GCMPN-SAF) and the block-sparse GCMPN-SAF (BS-GCMPN-SAF) algorithms are also developed. Moreover, the convergence analysis of the proposed algorithm is provided. Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.

Affine-Projection Lorentzian Algorithm for Vehicle Hands-Free Echo Cancellation

An adaptive estimation algorithm based on the Lorentzian norm is proposed for echo cancellation in vehicle hands-free communication systems and video teleconferencing systems, namely the affine-projection Lorentzian (APL) algorithm. By minimizing the Lorentzian norm of the a posteriori error vector with a suitable constraint on the weight vector and providing a dynamic Lorentzian-norm-controlling parameter, the proposed APL algorithm achieves robustness against impulsive disturbances and speeds up convergence for colored input signals. The computational complexity of the APL algorithm is analyzed and a fast recursive filtering method is employed to reduce its complexity. The stability analysis, based on energy-conservation arguments, shows that the APL algorithm converges. Furthermore, its tracking behavior is also investigated and a step size optimizing the tracking performance is derived. Simulation results agree well with the theoretical analysis. Simulation results for channel estimation and in-car echo cancellation scenarios demonstrate that the APL algorithm achieves better performance compared to the maximum-correntropy-criterion, affine-projection-generalized-maximum-correntropy, affine-projection-sign, affine-projection-like M-estimate, and Lorentzian adaptive filtering algorithms in various impulsive interference environments.

Adaptive interference compensation method

The article is devoted to the consideration of the method of adaptive compensation of interference to information transmission systems using space-time coding technology. When such systems use two or more antennas for transmitting information and the same number of antennas for receiving, it becomes possible to compensate for interference signals. The investigated compensation method is based on the introduction of adaptive filters into each of the input channels of the information transmission system. Adaptive filters are tuned to the process of the mixture of the useful signal and interference so that after the processing the sum of the interference components at the output of the compensator is minimized, and the useful signal does not deteriorate on average. The expressions that determine the algorithm of the adaptive noise compensator are given. The compensator for interference signals coming from a powerful airfield radar was built for the ground radio relay communication system with double antenna spacing. It has been shown experimentally that the compensator suppresses radio impulse interference from 30 to 40 decibels in different frequency channels of the microwave link.

The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy

Modulation frequency extraction of fluid machinery is significant, not only for the condition monitoring and fault diagnosis of cooperative targets in industrial fields but also for object detection and information prejudgment of non-cooperative targets in military applications. However, most existing demodulation methods tend to show poor performance due to the ubiquitous heavy Gaussian and non-Gaussian noise. Especially, narrowband demodulation methods such as Kurtogram are in a dilemma due to the signal characteristic of multi-wideband carrier wave in fluid machinery. In this paper, the Enkurgram is proposed for multiple demodulation frequency bands selection based on the combination of energy factor and shape factor. This method shows excellent demodulation capability in both simulation analysis and applications to fluid machinery. Firstly, an Amplitude-Modulated (AM) signal model with multi-wideband carrier wave is established. Unlike other narrowband demodulation methods, multiple non-overlapping frequency bands are selected by Enkurgram for demodulation based on the composite criterion of Spectral Energy (SE) and Spectral Kurtosis (SK). Moreover, the demodulation performance is quantified by the proposed Signal Characterization Ratio (SCR). Secondly, the effectiveness of Enkurgram is validated by simulation signals under different noise interference situations, including white Gaussian noise, stochastic impulse interference, periodic impulse interference, and contaminated modulation wave. Finally, the proposed method is verified by the actual signals from centrifugal pump and propeller, respectively. The analysis results prove that the Enkurgram has satisfactory demodulation capability in dealing with signals under low Signal-to-Noise Ratio (SNR) levels or with non-Gaussian noise, which is the Achilles’ heel of SK-based methods.

Impulsive noise modeling and robust receiver design

Interference is an important limitation in many communication systems. It has been shown in many situations that the popular Gaussian approximation is not adequate and interference exhibits an impulsive behavior. This paper surveys the different statistical models proposed for such an interference, that can generally be unified using the class of sub-exponential family of distributions, and its impact on the receiver design. Visualizing the optimal decision boundaries allows one to show the non linear effect induced by impulsive noise models, which explains the significant loss in receiver performance designed under the standard Gaussian approximation. This motivates the need to develop new receivers. We propose a framework to design receivers robust to a variety of interference types, both Gaussian and non-Gaussian. We explore three ways of thinking about such receiver designs: a linear approach; by approximating the noise plus interference distribution; and by mimicking the decision rule distribution directly. Except for the linear approach, the other designs are capable of replicating the non-trivial optimal decision regions to different extents. The new detection algorithms are evaluated via Monte Carlo simulations. We focus on four efficient architectures, including the parameter estimations: Myriad, Normal Inverse Gaussian, p-norm and a direct estimation of the likelihood ratio function. They exhibit good performance, close to the optimal, in a large range of situations demonstrating they may be considered as robust decision rules in the presence of heavy tailed or impulsive interference environments.

A new robust delayless subband adaptive filtering algorithm with variable step sizes for active control of broadband noise

Conventional delayless subband active noise control (ANC) systems can be degraded by the unexpected impulsive disturbances in the reference signal and/or the residual error signals despite the fact that the step sizes have been normalized by the variances of subband signals. Another inherent drawback is that an undesirable delay is introduced into the weight update path by using analysis filter banks, which reduces the upper bound of the step sizes thus limits the convergence rate. This paper proposes a novel robust and effective ANC algorithm configured in the delayless subband architecture for broadband noise cancellation. Instead of real-time providing threshold(s) on the reference and/or error signal as applied in most of the existing ANC algorithms for robustness, an online tuning scheme of bounded step sizes for the adaptive learning is developed for better convergence and outlier suppression. Box-constraint and time-averaging scheme deployed in the step size tuning guarantee robustness without requiring very accurate a priori information of the noises which might be corrupted by the impulses in real-world applications. In particular, this paper presents the stability and convergence analysis of the proposed closed-loop ANC system. Moreover, the computational complexity advantage is justified when compared to other state-of-the-art robust ANC algorithms. Numerical simulations are performed to validate the enhanced performance of the proposed algorithm for various colored noises with or without impulsive interferences.

Development of the Analysis Procedure of the Efficiency of Methods for Digital Filtering of the Incoherent Scatter Signal

The article considers the methodological features of creating procedures that help in the implementation of effective methods of recognition and suppression in the input signal, which are recorded by the incoherent scatter radar, noise and impulse interference. It is known that the frequency band of the input signal depends on the observation period of the ionosphere, as well as on its height above the Earth’s surface. Therefore, approaches to using analog filters with time-controlled characteristics in a radar receiver are being practiced. These filters, however, require a priori information about of the ionosphere state. At the present time, it is possible to obtain digital samples of a signal with a very small sampling step (tens of thousands of samples during a radar range sweep) using high-speed analog-to-digital conversion. It is also possible to record these readings in the computer memory relative to each sweep. This gives the prospect, based on the results of the experiment, to carry out auxiliary adaptive digital filtering of the obtained data. In this case, filter properties are selected for each altitude range in order to obtain the maximum possible signal-to-noise ratio. In order to introduce high-quality digital filtering methods, this development is aimed at creating a special software-algorithmic procedure, which allows you to control the effectiveness of the proposed filtration methods. The essence of this procedure is that according to the amplitude-frequency and phase-frequency spectra set by the researcher, the corresponding model of the scattering signal is synthesized and its autocorrelation function is calculated. These characteristics in the following steps as reference are used for comparison with similar characteristics, but obtained from the distorted scattering signal. The experimenter has the ability to apply noise and impulse interference to the scattered signal and to test the digital filtering method he proposed. All these steps in software implementation are accompanied by a clear graphical visualization of the results obtained.

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

A Blind Demodulation Algorithm for Underwater Acoustic MPSK Signal

Multiple phase-shift keying (MPSK) is a common carrier modulation in underwater acoustic (UWA) communication. This paper proposes a robust blind demodulation algorithm to improve the performance and practicability of blind demodulation of MPSK signals under impulsive noise and UWA sparse multipath channels. The proposed algorithm adopts a $T$ /2-spaced blind equalizer based on quasi-affine projection, so it has strong adaptability to sparse UWA multipath channels. First, the received signal is preprocessed to suppress the impulsive interference, and the parameters are estimated. Then, timing synchronization is performed point by point based on sliding discrete Fourier transform (SDFT) to avoid equalizer performance degradation or even lock-loss caused by large clock frequency deviation and long-time error accumulation. Based on this, the over-sampling signal with two fixed sampling points per symbol is obtained. Meanwhile, following the idea of memory improved proportionate affine projection algorithm and normalized factor, the cost function of the blind equalizer is constructed by using the $l_{0}$ norm penalty on the tap coefficients. In this way, the updating formula of the tap coefficients is derived, and the blind equalization is then conducted on the signal after timing synchronization to eliminate or weaken the channel influence. Finally, the residual frequency offset and phase offset of the carrier are removed through the M-power transform and the second-order decision feedback digital phase-locked loop. Simulation experiments and practical signal demodulation results indicate that the proposed algorithm achieves higher BER performance and modulation parameter robustness for impulsive noise and UWA multipath channel.

A novel correntropy-based band selection method for the fault diagnosis of bearings under fault-irrelevant impulsive and cyclostationary interferences

Demodulation analysis is one of the most effective methods for bearing fault diagnosis. However, in practical applications, the interferences from ambient noises or other rotating components may create great challenges to demodulation analysis and thus decrease its effectiveness. Generally, a selection procedure for the most informative frequency band (IFB) is usually implemented in advance to extract the fault features that are hidden by the interferences. The fast kurtogram (FK) has been utilized as a benchmark for the IFB selection. Although designed to identify the most impulsive part of the signal, the FK is inevitably affected by the fault-irrelevant impulsive and cyclostationary interferences due to the dual sensitiveness to the impulsiveness and cyclostationarity of the kurtosis, and thus it may produce a misleading band for demodulation. To address this issue, a novel and robust IFB selection method based on the fault energy of correntropy (named FECgram) is proposed in this paper to replace the FK, through which the IFB can capture the fault symptom without being influenced by the fault-irrelevant impulsive and cyclostationary interferences. The superiority of the FECgram in combination with the squared envelope spectrum (SES) is validated on both simulation data and three different challenging experimental datasets.

Kernel adaptive filtering algorithm based on Softplus function under non-Gaussian impulse interference

Kernel adaptive filters are a class of powerful nonlinear filter developed in reproducing kernel Hilbert space (RKHS).The Gaussian kernel is usually the default kernel in KAF algorithm, because the Gaussian kernel has the universal approximation. However, in previous research the kernel adaptive filtering algorithms were mostly based on mean square error criterion and assumed to be in a Gaussian noise environment. When environmental noise is changed, the performance of conventional kernel adaptive filtering algorithm based on mean square error criterion is seriously reduced to failure due to the interference of non-Gaussian noise and the influence of inappropriate non-Gaussian modeling. Therefore, it is important to develop a new method of suppressing the noise of non-Gaussian signals. In this paper, a new kernel fractional lower power adaptive filtering algorithm is proposed by combining the benefits of the kernel method and a new loss function which is robust against non-Gaussian impulsive interferences and has fast convergence under a similar stability condition. The proposed SP-KFLP algorithm generates a new framework of cost function which combines the Softplus function with the KFLP algorithm by updating its weight vector according to the gradient estimation while nonlinear saturation characteristics of output error are used. Compared with the features of sigmoid function the features of the Softplus function guarantee the SP-KFLP an excellent performance for combatting impulsive interference and speeding up the convergence rate. In the kernel fractional low power criterion the reciprocal of the system error is used as the coefficient of the weight vector update formula, and the method of error burst is used to make the weight vector not update to resist the impulse noise. The mean square convergence analysis for SP-KFLP is conducted, and a sufficient condition for guaranteeing convergence is therefore obtained by using the energy conservation relation. The proposed algorithm is very simple computationally. Simulations in a system identification show that the proposed SP-KFLP algorithm outperforms the kernel least-mean-square algorithm, kernel fractional lower power algorithm, and sigmoid kernel fractional lower algorithm in terms of convergence rate and the robustness of against impulsive interference. The proposed algorithm improves not only the capability of resisting impulsive interference, but also the convergence rate. In other words, the contradiction between convergence and tracking performance stability is well taken into account, and the performance under Gaussian noise is also better than the performance of the traditional kernel adaptive algorithm.

The Mkurtogram: A Novel Method to Select the Optimal Frequency Band in the AC Domain for Railway Wheelset Bearings Fault Diagnosis

A wheelset bearing is one of the main components of the train bogie frame. The early fault detection of the wheelset bearing is quite important to ensure the safety of the train. Among numerous diagnostic methods, envelope analysis is one of the most effective approaches in the detection of bearing faults which has been amply applied, but its validity greatly depends on the informative frequency band (IFB) determined. For the wheelset bearing faulty signal, it is often difficult to identify the IFB and extract fault characteristics due to the influence of complex operating conditions. To address this problem, a novel method to select optimal IFB, called the Mkurtogram, is proposed for railway wheelset bearings fault diagnosis. It takes the multipoint kurtosis (Mkurt) of unbiased autocorrelation (AC) of the squared envelope signal generated from sub-bands as assessment indicator for the first time. The fundamental concept which inspires this proposed method is to make full use of regular periodicity of AC of squared envelope signal. In the AC domain, the impulsiveness and periodicity, two distinctive signatures of the repetitive transients, have achieved a united representation by Mkurt. A simulated signal with multiple interferences and two experimental signals collected from wheelset bearings are applied to verify its performances and advantages. The results indicate that the proposed method is more effective to extract the wheelset bearings fault feature under complex interferences. It can not only decrease the influence of large impulse interference and the discrete harmonics interference, but also effectively overcome the influence of amplitude fluctuation caused by variable working conditions. Moreover, based on the periodic directivity of Mkurt, the proposed method also can be applied to the compound faults diagnosis of the wheelset bearing.

Diffusion affine projection maximum correntropy criterion algorithm and its performance analysis

The convergence performance of the conventional diffusion affine projection algorithm (DAPA) will be affected in impulsive noise environment. To overcome this shortcoming, in this paper, a diffusion affine projection maximum correntropy criterion (DAPMCC) algorithm is derived by using the maximum correntropy criterion in the diffusion network. The convergence of the DAPMCC algorithm in the mean and mean-square sense is studied, providing a constrained range for the step-size. Meanwhile, the theoretical steady-state mean square analysis of the proposed algorithm shows that the steady-state mean square deviation (MSD) is determined by parameters such as step-size and projection order. Simulation results demonstrate that the DAPMCC algorithm shows desirable estimation performance under impulsive interference environment, and the theoretical steady-state analysis is able to provide an accurate prediction.

NOMA-Based IoT Networks: Impulsive Noise Effects and Mitigation

The rise of the Internet of Things (IoT) presents important challenges for future radio networks. Non-orthogonal multiple access (NOMA), which allows the network to support more than one user per orthogonal resource element, was recently proposed as a promising solution that can ultimately support the daunting requirements of such networks including massive connectivity, high spectral efficiency, and low latency. Nevertheless, numerous ultra-high-reliability applications of IoT present environments that are hampered by impulsive electromagnetic interference, referred to as impulsive noise. Such noise is known to cause degradation to the overall system performance. Moreover, given the non-orthogonal multiplexing in NOMA, such noise is expected to have a relatively more pronounced impact on the system performance. Therefore, this article sheds light on the performance degradation and mitigation of impulsive noise in the context of NOMA-based IoT networks. It proposes a multistage nonlinear processing approach specifically designed for OFDM-based PDM-NOMA systems. To obtain the optimum threshold of the corresponding users, we propose a deep learning approach to estimate the impulsive noise parameters from the received OFDM symbol. This information can consequently be used to evaluate the corresponding optimal threshold using Siegert's ideal observer criterion. Finally, this work sheds light on potential opportunities and challenges that are expected to arise during the implementation of NOMA in impulsive environments.

Steady State Mean Square Analysis of Standard Maximum Versoria Criterion Based Adaptive Algorithm

The Maximum Versoria Criterion (MVC) based adaptive algorithm has recently gained the attention of researchers because of its robustness against impulsive interference and reduced complexity. In this brief, the steady-state Excess Mean Square Error (EMSE) analysis of the standard MVC algorithm is done using the energy conservation relation. For the Gaussian noise case, quadratic equation that can provide the exact value of theoretical steady state EMSE is obtained without any assumptions other than the conventional ones and the Price's theorem. For the non-Gaussian case, an approximate solution using Taylor's expansion is derived. Experimental results validate the obtained theoretical findings.

Model-based detection of soft faults using the smoothed residual for a control system

Soft faults are a very common failure mode in electro hydraulic servo control systems in aero engines, including component characteristic drift, performance degradation and so on. Traditional fault detection approaches for soft faults haveproblems of time delay, inaccuracy and false alarms. In order to solve these problems, a model-based detection approach for soft faults is proposed in this paper. Firstly, a robust, time-saving and reliable modeling method is proposed. This method utilizes a mechanism model and a data-driven correction model to characterize the main features and uncertain features of the practical system, respectively. The data-driven correction model is implemented by a backpropagation neural network, which adopts the input of the actual system and the output of the mechanism model as its training data, and adopts the output of the actual system as its training label. This neural correction model plays an important role in compensating for the deviation between the mechanism model and the practical system. Secondly, a robust fault observer, which is based on the residual between the measurement output of the practical system and the model output of the hybrid mechanism–neural network model, is designed to detect the soft fault. The squared residual is smoothed by a median exponential filter in order to eliminate the disturbances of impulsive interference or noises in measurement. Finally, the detection of soft faults is implemented by comparing the smoothed residual with a presetting threshold. Several simulations have been performed to verify the effectiveness of the proposed method, and the simulation results prove the advancement of the proposed method.

An Edge Computing Method for Partial Discharge under the Power Internet of Things

The recognition of impulse interference for the partial discharge data on the edge computing platform can effectively reduce the false alarm rate in the risk assessment of gas insulated switchgear (GIS). Therefore, an edge computing method for partial discharge is proposed in the paper, which uses the similarity computing among the aggregation of data for each sensor after clustering to match homologous signals. Finally, the algorithm will jointly locate the interference signal and recognize the impulse interference signal by comparing the received signal strength indicator (RSSI) of multiple UHF sensors in/out of GIS equipment. The training set, being consisted of partial discharge and interference signal from the test, is used to contrast the algorithms on the edge computing platform, the result of which indicates that the accuracy rate of the recognition of the impulse interference with the proposed algorithm is 81.4% on the computing platform, with the average processing time for each sample of 7.15s.

A Variable Step Size for Maximum Correntropy Criterion Algorithm with Improved Variable Kernel Width

The maximum correntropy criterion (MCC) algorithm has popularly been used in suppressing impulsive noise. It mainly relies on the choice of step size and kernel width. With the study of step size, this paper proposes a novel algorithm that updates the step size based on constructing a variable step size function that uses the moving weighted average algorithm to keep performance stable and sigmoid function to speed the convergence rate. The proposed algorithm is robust against impulsive noises and generates a large step size to accelerate the convergence in iteration beginning, and system mutation makes the system identification performance more stable when the error is small. Based on the improved variable kernel width maximum entropy criterion (IVKW‐MCC) algorithm, which overcomes the shortcoming of how to choose a reliable kernel width in the MCC algorithm, simulation results are compared with other MCC algorithms under impulsive noise interference. Simulations in the system identification scenarios show that the proposed algorithm has better performance in improving the convergence speed over other algorithms. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.