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To protect civilian Global Positioning System (GPS) users from spoofing, the Air Force Research Lab has developed the chips-message robust authentication (Chimera) signal enhancement for the GPS L1C signal. With Chimera, standalone receivers that only have access to the GPS signal will be able to authenticate their received measurements once every 3 min, whereas users with access to an out-of-band source will be able to perform authentication once every 1.5 or 6 s. However, moving receivers typically rely on much faster real-time GPS update rates of 1-20 Hz. In this work, we design a spoofing-resilient filter framework that provides continuous and secure state estimation between Chimera authentication times. By leveraging self-contained sensors on-board the vehicle, such as an inertial measurement unit or wheel encoder, as well as the periodic Chimera authentication, our proposed filter determines how much to rely on the received unauthenticated GPS measurements for state estimation. In this respect, our filter relies more extensively on GPS measurements in order to improve real-time navigation performance and reduce localization errors when GPS signals are authentic, while successfully mitigating spoofing-induced errors during an experienced attack. We experimentally validate our proposed spoofing-resilient filter in a simulated test environment for a ground vehicle model with access to the 3-min Chimera channel, under various simulated spoofing attack scenarios. To the best of the authors' knowledge, this is the first adaptive filter proposed for Chimera that continuously leverages real-time GPS measurements in a spoofing-resilient manner. [ABSTRACT FROM AUTHOR]

The power quality of the grid-coupled photovoltaic system (GCPVS) is enhanced using a control scheme developed by employing a robust generalized soft-root-sign (GSRS) adaptive filter. Power quality (PQ) deterioration issues are common in the distribution grid due to the mix of irregular loads. The GSRS control scheme mitigates harmonics, compensates load unbalances, achieves unity power factor at the grid, and compensates load reactive power, thus leading to balanced sinusoidal currents at the grid side. The GSRS algorithm utilizes a soft-root-sign cost function to achieve less steady-state misalignment while estimating magnitudes of fundamental frequency load current components for PQ improvement. The designed single-stage GCPVS performs smooth power flow between local loads and the grid without necessitating an energy storage system. The control scheme initially meets load demand and then sends any surplus power to the grid. However, if the generated PV power falls below the load demand due to environmental conditions, the additional power for the load is drawn from the grid. Moreover, voltage source converter used in the GCPVS also provides the functionality of DSTATCOM, thus avoiding the usage of additional equipment. The proposed GSRS control scheme and the GCPVS are modeled in MATLAB/Simulink. A laboratory-designed experimental setup is used to carry out the experimental validation of the GSRS control scheme and found to be performing satisfactorily. The behavior of GCPVS is verified during steady-state and dynamic conditions. The GCPVS performance is verified with the standard IEEE-519-2022, and a comparison of the GSRS algorithm with other filtering algorithms is provided. [ABSTRACT FROM AUTHOR]

Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left–right, up–down and open–close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) can serve as a supplement to the radio frequency to address its inherent constraints. VLC can be easily implemented in vehicles using their existing LED components. Sunlight interference can cause significant variation in the background noise powers, leading to signal-to-noise ratio (SNR) degradation in vehicular VLC (VVLC) communication. Hence, it is necessary to improve the performance besides interference. Allan variance is a temporal analysis approach that can extract the noise from the received data. In this article, an Allan variance-based adaptive noise canceller for a VVLC system is proposed and evaluated. Based on the Allan variance obtained, the step size and the number of taps of the adaptive filter are adjusted for superior performance. This proposed filter effectively mitigates the pronounced autocorrelation induced by the noise interference. It yields about an 85% bit error rate (BER) reduction compared to the conventional filter. This improved convergence underscores the potential for enhanced communication performance and reliability. The balanced distribution of the Manchester encoded signal outperforms NRZ encoding. Thus, this work contributes a novel adaptive noise canceller design for a VVLC system and also provides valuable insights to the significance of Allan variance in the VLC system performance. [ABSTRACT FROM AUTHOR]

To improve the convergence speed and noise cancellation performance of the Delayed Filtered-x Least Mean Square (DFxLMS) algorithm in the hardware implementation of compressor noise cancellation system, an improved Delayed Filtered-x Least Mean Square algorithm based on field-programmable gate array (FPGA) is proposed. The algorithm uses a parallel finite impulse response (FIR) filter structure and DSP48E1 module to implement the parallel multiplication and addition operation, which decreases hardware resource consumption and increases the system's processing speed. The cut set retiming technique reallocates the location of delay units to streamline the algorithm structure, reduce the critical path delay and increase the system data throughput. Moreover, through the adaptive filtering of input and feedback signals of the filter module, the noise suppression effect of the algorithm is improved. The results show that the maximum noise cancellation of the algorithm is 10 dB in the compressor noise cancellation system, which is 3 dB higher than the DFxLMS algorithm, and has 1.5 times higher data throughput. In addition, the Lookup Table (LUT), Flip-Flop (FF), and power consumption decreased by 18.5%, 33.6%, and 26.6%, respectively, and converged 1.8 times as fast. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a fractional delay (FD) Newton structure for Lagrangian interpolation in single phase grid integrated photovoltaic (PV) system. The structure has been designed for achieving shunt compensation and as a phase locked loop circuit for synchronization. The designed controller performs multiple tasks of feeding the active power to meet the load demand and simultaneously providing reactive power compensation, power factor improvement, and compensating other power quality voltage and load current harmonics. Further, synchronization poses a severe challenge in integrating the PV power electronic converters to the grid. The FD filter is used to discretize the continuous time signal at any time instant using interpolation algorithm employing the non‐integral value of the sampling rate delays. The FD delay filter is made adaptive and self‐learning by using least mean square algorithm. The designed synchronization technique meets the objective of faithfully tracking the phase of the grid voltage under all grid disturbances such as under voltage, overvoltage, frequency variations, and phase angle disturbances in a precise manner. [ABSTRACT FROM AUTHOR]

Sleep apnea is probably the most common respiratory disorder; respiration and blood oxygen saturation (SpO2) are major concerns in sleep apnea and are also the two main parameters checked by polysomnography (PSG, the gold standard for diagnosing sleep apnea). In this study, we used a simple, non-invasive monitoring system based on photoplethysmography (PPG) to continuously monitor SpO2 and heart rate (HR) for individuals at home. Various breathing experiments were conducted to investigate the relationship between SpO2, HR, and apnea under different conditions, where two techniques (empirical formula and customized formula) for calculating SpO2 and two methods (resting HR and instantaneous HR) for assessing HR were compared. Various adaptive filters were implemented to compare the effectiveness in removing motion artifacts (MAs) during the tests. This study fills the gap in the literature by comparing the performance of different adaptive filters on estimating SpO2 and HR during apnea. The results showed that up-down finger motion introduced more MA than left-right motion, and the errors in SpO2 estimation were increased as the frequency of movement was increased; due to the low sampling frequency features of these tests, the insertion of adaptive filter increased the noise in the data instead of eliminating the MA for SpO2 estimation; the normal least mean squares (NLMS) filter is more effective in removing MA in HR estimation than other filters. [ABSTRACT FROM AUTHOR]

Within vertebrates, central pattern generators drive rhythmical behaviours, such as locomotion and ventilation. Their pattern generation is also influenced by sensory input and various forms of neuromodulation. These capabilities arose early in vertebrate evolution, preceding the evolution of the cerebellum in jawed vertebrates. This later evolution of the cerebellum is suggestive of subsumption architecture that adds functionality to a pre-existing network. From a central-pattern-generator perspective, what additional functionality might the cerebellum provide? The suggestion is that the adaptive filter capabilities of the cerebellum may be able to use error learning to appropriately repurpose pattern output. Examples may include head and eye stabilization during locomotion, song learning, and context-dependent alternation between learnt motor-control sequences. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for "low, slow and tiny" moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method. [ABSTRACT FROM AUTHOR]

The standard proportionate normalized least mean square (PNLMS) adaptive algorithm suffers from convergence performance limitation due to a constant step-size during the convergence period. In this paper, a switching-based variable step-size PNLMS is proposed to improve the convergence performance in sparse system identification. To adjust the step-size, the convergence performance of PNLMS is first analysed in the statistical sense and by exploiting the analysis, a switching-based method is then proposed, which brings about a fast convergence towards the desired steady-state mean-square weight deviation. The step-size reduces during the convergence period in a few steps, while in the case of abrupt change in the system impulse response, the step-size increases to its initial value. A sub-band version of the proposed adaptive algorithm is further proposed for highly correlated input signals. Simulation results confirm the superiority of the proposed full-band and sub-band algorithms in terms of convergence performance compared to some competing adaptive algorithms. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an innovative concept of a high‐speed and higher order adaptive filter. A fully analog artificial neural network handles the adaption by using a filter bank for filtering and learning through analog feedback inspired by backpropagation. There is no clock control used in this concept, which makes real‐time adaption possible even for high frequencies. Its functionality is shown by several electrical circuit simulations involving component inaccuracies as well as a commonly used filter example. [ABSTRACT FROM AUTHOR]

A new method using three dimensions of cloud continuity, including range dimension, Doppler dimension, and time dimension, is proposed to discriminate cloud from noise and detect more weak cloud signals in vertically pointing millimeter-wave cloud radar observations by fully utilizing the spatiotemporal continuum of clouds. A modified noise level estimation method based on the Hildebrand and Sekhon algorithm is used for more accurate noise level estimation, which is critical for weak signals. The detection method consists of three steps. The first two steps are performed at the Doppler power spectrum stage, while the third step is performed at the base data stage. In the first step, a new adaptive spatial filter combined with the Kuwaraha filter and the Gaussian filter, using the ratio of mean to standard deviation as the adaptive parameter, is applied to initially mask the potential cloud signals to improve the detection performance at the boundary of cloud and noise. Simulations of boundary cases were performed to compare our adaptive filter and normal Gaussian filters. Box filters are used in steps two and three to remove the remaining noise. We applied our method to cloud radar observations with TJ-II cloud radar at the Nanjing University of Information Science & Technology. The results showed that our method can detect more weak cloud signals than the usual methods, which are performed only at the Doppler power spectrum stage or the base data stage. [ABSTRACT FROM AUTHOR]

The tobacco drying process in the cigarette production has an important effect on the final product quality. Therefore, the intelligent control methods have been widely investigated to ensure the stability of tobacco's outlet moisture content. The existing work mostly uses a relatively fixed set point of the outlet moisture content for different tobacco batches, which can lead to unforeseen product quality after several processes following the drying process and inaccessible amount of dehydration for the rotary dryer. Some tobacco moisture prediction methods have been studied recently while the relationship with the intelligent control methods remain largely unexplored. To deal with these issues, a novel method is proposed in this paper to identify the optimal set point of the drying outlet moisture content for each tobacco batch. An encoder-decoder model is first developed to forecast the post-drying moisture trajectory. Then, an adaptive filter with specially designed mechanisms and a confidence interval of the dehydration level are constructed to obtain the design constraints. Based on all above, a constrained optimization problem is formulated and solved by the genetic algorithm. Extensive experiments on 895 tobacco batches from a large cigarette factory are carried out, which involves both algorithmic evaluation and field test. It turns out that the proposed method achieves the superior performance and leads to an improvement of the product quality in real production. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop an effective method for onboard absolute orbital elements estimation of the non-cooperative target, which may beneficial to collision avoidance. The proposed estimator consists of two parts: the target acceleration observer and the orbital elements filter, where the observer generates partial input for the filter. For the estimator, the data source comes from the orbit determination information of the obstacle-avoidance-satellite and the relative measurement information of the non-cooperative target. These data are fed into an observer to estimate the maneuvering acceleration of the target. Then, the square root cubature adaptive H ∞ information filter (SCAHIF) for target-orbital-elements estimation under the complicated noise condition is designed, which is driven by the observer outputs and the target data source. The performance of the overall "observer-enhanced SCAHIF" (OSCAHIF) is evaluated through three scenarios where the target is under zero maneuvering thrust, constant maneuvering thrust, and time-varying maneuvering thrust. Test results indicate that the proposed OSCAHIF provides a feasible solution to real-time absolute orbital elements estimation of the non-cooperative target with the presence of time-variant mixed Gaussian white noise. • OSCAHIF is proposed for orbit element and thrust estimation of noncooperative target. • An observer is developed for maneuver acceleration estimation. • The adaptive H-infinity method is introduced to improve the robustness. • The observer, square root cubature IF, and adaptive H-infinity method forms OSCAHIF. [ABSTRACT FROM AUTHOR]

Currently, security enhancement of biometric systems is an important issue that deserves consideration. This is attributed to the threats facing traditional common recognition systems, which depend on Personal Identification Numbers (PINs) that can be stolen easily. Utilization of original biometrics to access user services may lead to loss of the biometrics forever, if hacking attempts succeed in gaining access to the storage database of original templates. To address this concern and to avoid the utilization of original biometrics, we keep them away from being compromised through the utilization of cancelable biometric templates. This paper introduces a novel methodology for user authentication with multiple biometrics to generate distorted non-invertible cancelable templates to be stored in the database. The proposed framework begins with Discrete Cosine Transform (DCT) to achieve data compression in a multi-biometric scenario. After that, Double Random Phase Encoding (DRPE) is applied to increase the security level of the generated distorted encrypted templates. Finally, an adaptive filter is used to generate masking patterns for biometric templates. The patterns are uncorrelated, which improves the security level against identity theft and provides better identification performance. Simulation results give a promising performance of the proposed cancelable biometric recognition framework with a high Area under the Receiver Operating Characteristic curve (AROC) of 99.9824% and an Equal Error Rate (EER) of 0%. In addition, other statistical evaluation metrics have been considered to reveal superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

Active noise reduction in the existential of impulsive noise poses to be a serious challenge in a distributed scenario. The conventional centralized multichannel active noise control (MANC) and diffusion filtered-x least mean square (F-xLMS) based distributed active noise control (DANC) systems exhibit deteriorated performance in the existence of impulse noise and outliers. In this regard, a diffusion least logarithmic hyperbolic cosine function (D-Llncosh) based algorithm for DANC system is developed with the goal of mitigating noise in such an environment. It utilizes natural logarithmic of hyperbolic cosine function integrating the mean square error (MSE) and mean absolute error (MAE) as the cost function depending upon the parameter value. The weight update rule of the introduced D-Llncosh-based DANC system is hereby derived. The convergence analysis of the suggested method is also carried out. The proposed system is validated for various types of impulsive noise under different scenarios. The extensive simulation results assert the efficacy of the posited DANC system in comparison with the existing techniques. The suggested method achieves quicker convergence and approximately a 1–5 dB improvement is the noise reduction for different noise scenarios as evident from the obtained results. [ABSTRACT FROM AUTHOR]

Objective Method Results Discussion Conclusion The digital sound recording of several heart sounds (HSs) is named phonocardiogram (PCG) signal. Analysing these PCG signals is essential for diagnosing diverse sorts of heart disorders. Nevertheless, owing to troubling surrounding noise signals, PCG signal recording is challenging. So, before utilising PCG for advanced processing, de‐noising the PCG signal is executed. A new sliding window adaptive noise cancellers (SWANC)‐centred filter technique is proposed in this paper for de‐noising along with recovering the PCG signal effectively.Utilising the least mean square (LMS), an SW optimum adaptive filter (AF) structure is introduced in this work for estimating a De‐noised signal (DS) with better accuracy. Here, via the SWAF stage, a noisy signal is processed. An SW of fixed duration slides over the signal; also, the signal is filtered utilising the AF in each window. Utilising this SWAF architecture, this method approximates the PCG signal's clean version with better accuracy.The proposed robust SWAF is analogized to experimental PCG signals that were corrupted by Gaussian noise (GN) together with pink noise (PN) with distinct noise levels (NLs). From the physionet database, the experiential data are acquired. The outcomes exhibited that a remarkable performance was attained by the robust SWAF model.A 2–10 times decrease in MSE values was attained by the proposed filter structure when analogized with conventional LMS filter configuration. Further, the SNR is improved by 3 times and comparatively, the PSNR enhancement was 4%–25%. The association betwixt the clean signal along with its estimate is greater than 0.92.A cost‐efficient hardware installation of ANC with higher accuracy was offered by the SW LMS AF model. For obtaining desired convergence speed and accuracy, such models are tested for real‐time performances in the future. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are widely used in communication, medical treatment, radar and detection. With the vigorous development of computer science and intelligent technology, wireless sensor networks are also constantly improving in the development. Sensor networks are prone to noise interference when input signals, which will affect the estimation accuracy of the network. In order to enhance the signal of sensor network and improve its accuracy, a distributed filtering algorithm based on fusion adaptive weighting is proposed. Before building the model, the experiment first studied the three traditional adaptive filtering algorithms, LMS, RLS and AP, as the basis for building the experimental model. Then, combined with the distributed characteristics of the sensor network, the attributes of the nodes and their influence in the network were considered in the experiment, and the importance and support of the nodes were linearly weighted to obtain the estimated certainty of each sensor node to the target. Finally, a fusion adaptive weighted distributed filtering algorithm is constructed in the experiment. The simulation experiment verifies that the constructed model can reduce the noise interference to a certain extent, which is conducive to the enhancement of its network signal, and its error estimation accuracy is also improved. [ABSTRACT FROM AUTHOR]

The grid-connected inverter is the key component for the reliable and safe operation of grid-interconnected renewable energy systems. In order to ensure that the inverter output current is in-phase synchronized with the grid voltage, a highly efficient and fast control strategy is required. This paper proposes an algorithmically driven approach for designing the adaptive controller for a grid-connected DC/AC inverter. The controller consists of two adaptive IIR filters based on the LMS algorithm and has two modes of operation. During the learning mode, the controller uses the filters to estimate the coefficients of the inverse transfer function of the PWM-driven inverter at several grid frequencies within a specific range. During the online mode, the controller generates the driving signal for the inverter block using a set of learned coefficients corresponding to the current grid frequency. By constantly monitoring the grid voltage and reconfiguring the controller accordingly, a fast adaptation of the inverter output current to grid frequency variations is achieved. For grid frequency variations in the range of 48 to 52 Hz, the MATLAB simulation results show that the phase difference between the grid voltage and inverter output current waveforms becomes less than 1 degree after at most four periods. [ABSTRACT FROM AUTHOR]

Tremors are a symptom of several disorders of the central and peripheral nervous systems, such as Parkinson's disease (PD). Exoskeletons have been investigated as noninvasive tremor suppression alternatives to medication and surgery. The challenge in musculoskeletal tremor suppression is attenuation of tremor motion without impeding the patient's voluntary motion. Linear low-pass filters (LPFs) are commonly used for tremor removal. This study presents an alternative method based on an adaptive filter. We compare the effectiveness of the LPFs and adaptive filter on the recorded acceleration signals from an online database. [ABSTRACT FROM AUTHOR]

In this paper, several methods about the active noise control (ANC) system are studied. The common adaptive recognition system based on the variable step filter x least mean square (FxLMS) algorithm are elaborated. By introducing the momentum term into the variable step algorithm, the value range of the variable step is increased, so as to speed up the convergence of the algorithm and improve the effect of noise reduction. The weight of the control filter based on the improved logarithmic function variable step size least mean square (VSSLMS) algorithm is updated in the improved second channel online recognition algorithm. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results show that the improved algorithm structure could improve the convergence speed and reduce the noises of the whole system at three different primary noises. The initial smooth noise signal, the noise signal of increasing amplitude, and the abrupt noise signal are utilized to verify the convergence speed and the noise reduction effect of the modified ANC method. Numerical results under three different primary noises show that the improved algorithm structure could improve the convergence speed and reduce the noise of the whole system. [ABSTRACT FROM AUTHOR]

Systems for atmospheric research and wireless communication use the High Frequency (HF) radio spectrum. At these frequencies, typically up to 20 MHz, the ambient electromagnetic noise is stronger than the noise generated by the receiver itself, thereby limiting the sensitivity of the instruments. Especially in urban areas, the noise level is high. In remote rural environments, where artificial noise sources are absent, a much lower noise level is observed. It has been shown that this noise arrives via ionospheric propagation and consists of impulsive noise from lightning and a background component that resembles additive white Gaussian noise. To establish the absolute field strength of this background noise component, a direction- and polarization-agnostic antenna is realized by adding the power of two orthogonal antenna elements in the digital domain. To suppress radio signals arriving via ionospheric propagation—of which the spectral and temporal aspects are not known a priori—a novel adaptive filter is demonstrated that separates the background noise from the radio signals in the joint frequency-time domain. This method is demonstrated using measurements from a polarimetric experiment on 7 MHz in a remote rural area in Catalonia. The results are submitted to the International Telecommunication Union for the validation of ambient noise models. [ABSTRACT FROM AUTHOR]

In recent years, speech processing resides a major application in the domain of signal processing. Due to the audibility loss of some speech signals, people with hearing impairment have difficulty in understanding speech, which reintroduces a crucial role in speech recognition. Automatic Speech Recognition (ASR) development is a major challenge in research in the case of noise, domain, vocabulary size, and language and speaker variability. Speech recognition system design needs careful attention to challenges or issues like performance and database evaluation, feature extraction methods, speech representations and speech classes. In this paper, HDF-DNN model has been proposed with the hybridization of discriminant fuzzy function and deep neural network for speech recognition. Initially, the speech signals are pre-processed to eliminate the unwanted noise and the features are extracted using Mel Frequency Cepstral Coefficient (MFCC). A hybrid Deep Neural Network and Discriminant Fuzzy Logic is used for assisting hearing-impaired listeners with enhanced speech intelligibility. Both DNN and DF have some problems with parameters to address this problem, Enhanced Modularity function-based Bat Algorithm (EMBA) is used as a powerful optimization tool. The experimental results show that the proposed automatic speech recognition-based hybrid deep learning model is effectively-identifies speech recognition more than the MFCC-CNN, CSVM and Deep auto encoder techniques. The proposed method improves the overall accuracy of 8.31%, 9.71% and 10.25% better than, MFCC-CNN, CSVM and Deep auto encoder respectively. [ABSTRACT FROM AUTHOR]

To improve the performance of identifying the block-sparse systems, firstly, a new sparsity aware block-sparse proportionate affine projection algorithm (BS-PAPA) with a fixed zero attractor is proposed in this paper. The proposed algorithm is obtained by integrating a weighted l 0 -norm penalty into the cost function of the BS-PAPA. Adding the penalty results in zero attraction terms in the weight update recursive equation of the BS-PAPA, which helps in the shrinkage of inactive coefficients. The proposed algorithm is named l 0 -block-sparse proportionate affine projection algorithm ( l 0 -BS-PAPA). The convergence analysis in the mean is derived for the l 0 -BS-PAPA. Secondly, for applications having time varying measurement noise, an adaptive zero attractor l 0 -BS-PAPA is also developed by adaptive optimisation of the zero attractor. This one is more robust to the varying degree of measurement noise than the former. A variable step-size l 0 -BS-PAPA is also introduced to further enhance the performance of the l 0 -BS-PAPA. Computer simulation experiments reveal that the l 0 -BS-PAPA outperforms the existing algorithms in block-sparse systems in terms of convergence rate, normalised misalignment, and tracking ability. [ABSTRACT FROM AUTHOR]

Nowadays wind energy production is growing fast, and the cost of operation and maintenance is growing fast also. Most wind turbines (WTs) are equipped with supervisory control and data acquisition (SCADA) system for system control and logging data. Huge amounts of data acquired for SCADA systems can be used for condition monitoring and fault detection (CMDF) by applying data mining methods. However, the collected data are not used effectively. Few researches are about input and output data optimization and proper feature selection. In this paper, the proposed method regards SCADA data as data points. Wavelet analysis is applied to the input signal to make noise reduction and uses recursive least square (RLS) filter to reduce false alarm rate. On the basis of the general model-based CMDF approach, the random forest algorithm is used to find the best input features. With these methods applied, a more precise output is obtained as well and greatly reduces the false alarm rate as well. Experiments are given with real SCADA data to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting from the superiority of correlated Kurtosis, FMD takes the impulsiveness and periodicity of fault signal into consideration simultaneously. First, a designed FIR filter bank by Hanning window initialization is used to provide a direction for the decomposition. The period estimation and updating process are then used to lock the fault information. Finally, the redundant and mixing modes are removed in the process of mode selection. The superiority of the FMD is demonstrated to adaptively and accurately decompose the fault mode as well as robust to other interferences and noise using simulated and experimental data collected from bearing single and compound fault. Moreover, it has been demonstrated that FMD has superiority in feature extraction of machinery fault compared with the most popular variational mode decomposition. [ABSTRACT FROM AUTHOR]

A novel sparse LMS algorithm for the sparse system identification is proposed to reduce the complexity of the existing algorithms. The proposed algorithm discards W(n) when the value of the current weight vector is within a certain range. The algorithm also optimizes the iterative update equation by using only the product term to calculate the value of W(n+1). The hardware implementation shows that the total logic elements number of the proposed algorithm is 60.06% less than the latest l0-ILMS algorithm, while the performance is nearly the same. [ABSTRACT FROM AUTHOR]

A signal enhancement method of vehicle wireless sensor network (WSN) based on adaptive filter is designed in this paper. By analyzing the vehicle WSN structure and node deployment mode, the signal operation process of WSN in the vehicle is determined, the relationship between Received Signal Strength Indication (RSSI) and distance is analyzed, and the signal of vehicle WSN is acquired. In order to reduce the workload of signal enhancement, set the signal strength judgment conditions of vehicle WSN, among the collected signals, the signal with poor intensity is taken as the research object. On this basis, the adaptive filter is used to remove the signal noise of vehicle WSN and enhance the signal strength. The signal enhanced by the proposed method has less noise and good stability. [ABSTRACT FROM AUTHOR]

Sunlight arrival time delay is an innovative measurement for celestial navigation, which provides the distance information between the spacecraft and the nearby celestial body. For deep space detectors, the time delay measurement noise is time-varying, and its statistics cannot be accurately determined. The too-large deviation between the measured noise covariance matrix (R) and the noise statistics seriously reduces navigation accuracy. A variational Bayesian implicit unscented Kalman filter (VBIUKF) method is proposed and applied to celestial navigation using time delay measurement in this paper. VBIUKF adaptively estimates R using the innovation sequence and the predicted measurement covariance matrix to make R closest to the measurement noise statistics. Simulation results indicate that VBIUKF can obtain high accuracy navigation results under time-varying measurement noise. [ABSTRACT FROM AUTHOR]

Nowadays, digital images play an increasingly important role in helping to explain phenomena and to attract people's attention through various types of media rather than the use of text. However, the quality of digital images may be degraded due to noise that has occurred either during their recording or their transmission via a network. Therefore, removal of image noise, which is known as "image denoising", is one of the primary required tasks in digital image processing. Various methods in earlier studies have been developed and proposed to remove the noise found in images. For example, the use of metric filters to eliminate noise has received much attention from researchers in recent literature. However, the convergence speed when searching for the optimal filter coefficient of these proposed algorithms is quite low. Previous research in the past few years has found that biologically inspired approaches are among the more promising metaheuristic methods used to find optimal solutions. In this work, an image denoising approach based on the best-so-far (BSF) ABC algorithm combined with an adaptive filter is proposed to enhance the performance of searching for the optimal filter coefficient in the denoising process. Experimental results indicate that the denoising of images employing the proposed BSF ABC technique yields good quality and the ability to remove noise while preventing the features of the image from being lost in the denoising process. The denoised image quality obtained by the proposed method achieves a 20% increase compared with other recently developed techniques in the field of biologically inspired approaches. [ABSTRACT FROM AUTHOR]

In this article, we explore a new extended Lienard-type planar system with "corrections" of the second kind Chebyshev's polynomial U n . The number and type of limit cycles are also studied. The discussion on the y (t) —component of the solution of the Lienard system is connected to searching for the solution of the synthesis of filters and electrical circuits. Numerical experiments, depicting our outcomes using CAS MATHEMATICA, are presented. [ABSTRACT FROM AUTHOR]

Noise contaminates and distorts measurements from wireless sensor networks (WSNs). The sensor node's computations and energy consumption are increased due to the noise in the signal, resulting in the sensor node's shorter lifespan. Therefore, efficient design is required to achieve noise reduction. The finite impulse response (FIR) filter is a significant part of signal preprocessing in the WSN to remove noise. Signal preprocessing could assist in minimizing the amount of energy used in communication between nodes while also improving data transmission efficiency. The multiplier block in an FIR filter involves the generation and addition of partial products (PP), which consumes a large area and enormous power. The most common adaptive filtering technique utilizes the least mean squares (LMS) algorithms. The multiply and accumulate (MAC) process is the spine of LMS adaptive filters. Using multiple MAC units can boost the system's speed, but the cost of the system rises as the multipliers take up a lot of space and consume more energy. When LMS is implemented as a completely reliable architecture on a hardware platform, the multiplier evolves into a bottleneck for higher-order filters, resulting in significant size, expenditure, and energy needs, rendering the design unsuitable for practical implementation. FIR filters are frequently realized using distributed arithmetic (DA) based scheme. DA-based designs replace multipliers with look-up tables (LUT), where the precomputed PPs is saved. LUTs are required for weight update and filtering processes. The size of the LUT rises exponentially with the increasing order of filters. To minimize the size of a LUT, offset binary coding (OBC) is utilized, resulting in smaller memory size. The proposed architecture is based on DA in which PPs of filter coefficients are pre-calculated, and these coefficients are saved in LUTs, and through the use of OBC, the weighted coefficients are updated. The proposed method does not decompose the LUT into two smaller LUT to achieve area and energy efficiency. Typically, each iteration requires recalculating all of the LUT's address locations. This research presents a novel method that iteratively updates the contents of LUT without rotating the address, making the FIR filter more energy efficient. Random access memory-based LUT is utilized to eliminate the physical address rotation. A method for reducing LUT access time is proposed. The proposed design significantly reduces area and energy consumption compared to the existing systems. Also, it saves a large amount of sliced LUT and flip-flops compared to conventional methods. Therefore, high-performance adaptive filtering applications like IoT-based WSN can benefit the most from the suggested approach. [ABSTRACT FROM AUTHOR]

Cognitive radio is considered a relevant communication paradigm to deal with the increasing demands in modern communications systems. Adaptive schemes are required to recognize channel conditions and to properly adjust main transmission parameters to improve the quality of communications. In this direction, blind algorithms to recover constellation, from phase-modulated signals, represent a means to implement cognitive capabilities to allow automatic modulation recognition (AMR) on receivers. Commonly, the most popular approaches for blind constellation recovery are based on a two-step scheme. The first step uses to equalize channel effects and reduce inter-symbol interference (ISI). The second step carries out constellation recovery utilizing phase locked loop (PLL) systems like the Costas Loop, then to classify the incoming signal. This work proposes a novel single-step blind adaptive filter solution, inspired by an adaptive interference canceler, for joint equalization and constellation symbol recovery from received phase shift keying (PSK) waveforms. Furthermore, we propose new coefficients update mechanisms based on the constant amplitude of PSK signals. The proposed solution exhibits reduced computational complexity compared to the state of the art and a reduced time of convergence. Additionally, the proposed scheme does not require a training sequence to operate properly. The obtained results clearly show that the proposed scheme significantly improves accuracy regarding phase symbol estimation and ISI reduction. [ABSTRACT FROM AUTHOR]

Neural interfaces, such as microarrays and probes, consist of many electrodes for stimulating and recording purposes simultaneously. The multi-functional neural interface can suffer from many types of artefacts and noise, such as long-term use, environment, surrounding instruments and living subjects. This paper proposes a filtering approach by enhancing the band-pass and band-stop selection of the Kaiser Window finite-impulse response (FIR) filter based on the occurrence histogram of spectrum bands of neuronal signals in all channels. The implementation of the approach shows a clear enhancement of electrocorticogram (ECoG) signals by keeping the most important features and components, such as the interictal spikes. [ABSTRACT FROM AUTHOR]

In this paper, an anti-jamming scheme based on transmitted multiple pulses through random initial phases and an adaptive iterative filtering algorithm is considered for counteracting range-velocity deceptive jamming in Pulse-Doppler (PD) radar system. This anti-jamming scheme relies on the fact that the digital radio frequency memory (DRFM) repeated jammer needs more than one pulse repetition interval PRI (at least one PRI) to identify the radar transmitted pulses, and therefore uses the captured pulses of the lag PRIs to reproduce multiple false targets. As a consequence, an adaptive-iterative algorithm to estimate the range-Doppler plane of the real targets in range dimension processing and Doppler dimension processing can appropriately suppress the deceptive false targets, which employed the lagged PRIs for the radar deception. Firstly, the proposed method achieves the optimal estimation of the true targets and false targets in the range dimension. Then, with the estimation information in the range dimension, the presented method estimates the range-Doppler plane of the true targets and false targets. Finally, we evaluate the performance of the proposed method as well as achieve a good trade-off between anti-jamming performance and computational complexity via numerical simulations. [ABSTRACT FROM AUTHOR]

To utilize the full second-order statistical information of the complex-valued signal, a widely linear complex-valued LMM (WL-CLMM) algorithm is proposed by using different M-estimate functions. The proposed WL-CLMM algorithm can process both circular and noncircular complex-valued signals in impulsive noise environments. Moreover, a novel adaptive threshold adjustment method for the M-estimate function is designed according to the probability density function of the complex-valued error signal. In addition, to decrease the sensitivity of the input signal to the performance of the algorithm, the normalized version of WL-CLMM (WL-CNLMM) has been developed. Then, we carry out the mean behavior and mean square behavior analysis of the proposed algorithms. Simulation results show that the proposed algorithms outperform some existing complex-valued algorithms and the theoretical results are well matched. [ABSTRACT FROM AUTHOR]

This paper presents a novel variable matrix-type step-size affine projection sign algorithm (VMSS-APSA) characterized by robustness against impulsive noise. To mathematically derive a matrix-type step size, VMSS-APSA utilizes mean-square deviation (MSD) for the modified version of the original APSA. Accurately establishing the MSD of APSA is impossible. Therefore, the proposed VMSS-APSA derives the upper bound of the MSD using the upper bound of the L 1 -norm of the measurement noise. The optimal matrix-type step size is calculated at each iteration by minimizing the upper bound of the MSD, thereby improving the filter performance in terms of convergence rate and steady-state estimation error. Because a novel cost function of the proposed VMSS-APSA was designed to maintain a form similar to the original APSA, they have symmetric characteristics. Simulation results demonstrate that the proposed VMSS-APSA improves filter performance in a system-identification scenario in the presence of impulsive noise. [ABSTRACT FROM AUTHOR]

The variable step size least mean square algorithm has been suggested since a number of years as a potential solution for improving the performance of least mean square algorithm. In this article, the variable step size least mean square algorithm is classified by the techniques which are used to update step size. Unfortunately, for variable step size least mean square algorithms with forgetting factor, a constant forgetting factor may slow down its convergence speed. For this reason, a variable forgetting factor method for variable step size least mean square is proposed in this article. First, the convergence analysis of a new variable step size least mean square algorithm with the variable forgetting factor is provided. Then, simulations expose the characteristics of this variable forgetting factor method. Last, a micro-vibration control experimental system is established. Four typical variable step size least mean square algorithms and their variable forgetting factor modified version are verified through experiments. The results show that the proposed variable forgetting factor method can effectively improve convergence speed while maintaining the steady-state performance of the variable step size least mean square algorithm with the constant forgetting factor. [ABSTRACT FROM AUTHOR]

The accurate extraction and analysis of slot harmonics caused by slotting in an induction motor are important for the motor's performance evaluation and state monitoring. However, the frequency distribution of rotor slot harmonics (RSHs) varies along with the operating states of the motor, such as motor speed and slip ratio, and the voltage and current signals of the motor only contain small-amplitude RSHs compared with other harmonics; both make it difficult to extract and analyze the RSHs accurately online. While offline extraction and filters with constant parameters are mainly utilized in available works, a novel adaptive extraction method for RSHs in a multiphase induction motor is proposed here to realize online RSH extraction under different speed and load conditions. In this paper, the RSHs in the multiphase induction motors are firstly modeled by using the magnetic potential permeability method, and the influence of a skewed rotor on RSHs is analyzed through a multisection method. Then, an adaptive extraction method of RSHs is proposed, which can effectively realize the online processing of RSHs of stator current. Finally, the experimental platform of a nine-phase induction motor has been used to verify the effectiveness of the proposed method under different speeds and load conditions, with a relative error of less than 1% in identifying the RSH frequency distribution. [ABSTRACT FROM AUTHOR]

The newly developed near-space vehicle has the characteristics of high speed and strong maneuverability, being able to perform vertical skips and a wide range of lateral maneuvers. Tracking this kind of target with ground-based radars is difficult because of the limited detection range caused by the curvature of the Earth. Compared with ground-based radars, satellite tracking platforms equipped with Synthetic Aperture Radars (SARs) have a wide detection range, and can keep the targets in custody, making them a promising approach to tracking near-space vehicles continuously. However, this approach may not work well, due to the unknown maneuvers of the non-cooperative target, and the limited computing power of the satellites. To enhance tracking stability and accuracy, and to lower the computational burden, we have proposed a Fast Distributed Multiple-Model (FDMM) nonlinearity estimation algorithm for satellites, which adopts a novel distributed multiple-model fusion framework. This approach first requires each satellite to perform local filtering based on its own single model, and the corresponding fusion factor derived by the Wasserstein distance is solved for each local estimate; then, after diffusing the local estimates, each satellite performs multiple-model fusion on the received estimates, based on the minimum weighted Kullback–Leibler divergence; finally, each satellite updates its state estimation according to the consensus protocol. Two simulation experiments revealed that the proposed FDMM algorithm outperformed the other four tracking algorithms: the consensus-based distributed multiple-model UKF; the improved consensus-based distributed multiple-model STUKF; the consensus-based strong-tracking adaptive CKF; and the interactive multiple-model adaptive UKF; the FDMM algorithm had high tracking precision and low computational complexity, showing its effectiveness for satellites tracking the near-space target. [ABSTRACT FROM AUTHOR]

Stereophonic acoustic echo cancellation (SAEC) applications of adaptive filters are complex compared to monophonic AEC due to the presence of an additional acoustic channel. In long room impulse response, SAEC is carried out with the help of more than one adaptive filter, each having hundreds to thousands of filter coefficients. A large number of filter weights result in degraded convergence and enhances filter design complexity. Multiple sub-filters (MSF) and variable tap-length (VT) algorithms are independently proposed for SAEC scenarios to address these issues. The MSF-based design improves the convergence; on the other hand, the VT algorithm optimizes the weight requirement for adaptive filters. Pseudo-optimum filter order is a common phenomenon in the VT algorithm, which results in undermodelling in SAEC applications. This paper analyses the convergence, mean-square convergence, and stability of an undermodelled final error VT-MSF-SAEC (FE-MSF-SAEC) and VT-MSF-SAEC. The mathematical analysis and the supported simulation with three variants of inputs represent the effect of pseudo-fractional undermodelling for a VT-MSF-SAEC. [ABSTRACT FROM AUTHOR]

In the complex and variable marine environment, the navigation and localization of autonomous underwater vehicles (AUVs) are very important and challenging. When the conventional Kalman filter (KF) is applied to the cooperative localization of leader–follower AUVs, the outliers in the sensor observations will have a substantial adverse effect on the localization accuracy of the AUVs. Meanwhile, inaccurate noise covariance matrices may result in significant estimation errors. In this paper, we proposed an improved Sage–Husa adaptive extended Kalman filter (improved SHAEKF) for the cooperative localization of multi-AUVs. Firstly, the measurement anomalies were evaluated by calculating the Chi-square test statistics based on the innovation. The detection threshold was determined according to the confidence level of the Chi-square test, and the Chi-square test statistics exceeding the threshold were regarded as measurement abnormalities. When measurement anomalies occurred, the Sage–Husa adaptive extended Kalman filter algorithm was improved by suboptimal maximum a posterior estimation using weighted exponential fading memory, and the measurement noise covariance matrix was adjusted online. The numerical simulation of leader–follower multi-AUV cooperative localization verified the effectiveness of the improved SHAEKF and demonstrated that the average root mean square and the average standard deviation of the localization errors based on the improved SHAEKF were significantly reduced in the case of the presence of measurement abnormalities. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive vector directional distance filter (AVDDF) is proposed. The AVDDF filter is developed to remove the 'salt and papper' impulsive noise in color images. This algorithm consists in first step to detect pixels that are likely to have been contaminated with noise by using a threshold value. In second step, after recognizing the corrupted pixels, the smallest angular‐magnitude distance is used to replace the noisy pixels. The proposed filter is tested with several standard color images which are contaminated with various levels of 'salt and pepper' impulsive noise (3%, 5%, 10%, 20% and 30%). The performance of the proposed filter is measured with peak signal‐to‐noise ratio (PSNR) and normalized color difference (NCD). The simulation results show that the proposed filter permits to ameliorate the performance of the classic vector directional distance filter (VDDF). Indeed, it provides an improvement by an average of 5% and 26% in the PSNR and NCD relative to the VDDF filter, respectively, with a small increase in the execution time by 6%. Besides, the AVDDF filter allows also an enhancement by an average of 2% and 14% in the PSNR and NCD relative to adaptive vector median filter (AVMF), respectively. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper analyzes the correlation matrix between the a priori error and measurement noise vectors for standard and augmented affine projection algorithms using a unified approach. This correlation stems from the dependence between the filter tap estimates and the noise samples, and has a strong influence on the mean square behavior of the algorithm. We show that the correlation matrix is upper triangular, and compute the diagonal elements in closed form, showing that they are independent of the input process statistics. Also, for white inputs we show that the matrix is fully diagonal. These results are valid in the transient and steady states, considering a possibly variable step-size. Our only assumption is that the filter order is large compared to its projection order and that the input signal is stationary. Using these results, we perform a steady-state analysis for small step size and provide a new simple closed-form expression for the mean-square error, which has comparable or better accuracy to many preexisting expressions, and is much simpler to compute. Finally, we also obtain expressions for the steady-state energy of the other components of the error vector. • Unifies treatment for linear and widely linear affine projection algorithms. • Analyzes of the correlation between the noise and the a priori error vectors. • Provides a simple and accurate mean-square error formulas. [ABSTRACT FROM AUTHOR]

• Impulsive mode is formally defined and its characteristics include band-limited and sparse energy distribution features. • New geometrical mean-based pq-mean as an optimization objective function is proposed to quantify impulsive mode. • Impulsive mode decomposition is innovatively proposed to decompose a signal into impulsive and non-impulsive modes. • Impulsive mode decomposition is essentially an adaptive filterbank for impulsive signal analysis. Pulse components commonly exist in natural signals and their extraction has received extensive concerns in many domains, such as machinery fault diagnosis, ECG denoising, non-destructive testing, chatter analysis, etc. Even though many signal decomposition methods (SDMs) have been applied to pulse component extraction, they are not originally tailored for pulse component extraction and cannot accurately and fully extract all pulse components. In this paper, impulsive mode decomposition (IMD) is originally tailored for adaptive pulse component extraction, and it can decompose a signal into impulsive modes and non-impulsive residual modes. This work mainly contributes three aspects: (i) A formal definition of impulsive mode; (ii) Geometrical mean-based pq-mean with four essential properties for quantification of impulsive modes; (iii) a novel iteratively-searching adaptive filterbank for extraction of impulsive modes. The effectiveness and ability of the proposed IMD are validated by a simulation case and three real-world application cases in machinery fault diagnosis and ECG signal denoising. Comparisons with variational mode decomposition, empirical wavelet transform, and minimum entropy deconvolution demonstrated the superiority of the IMD. The proposed IMD is promising to be used in various domains to extract pulse components of interest. [ABSTRACT FROM AUTHOR]

• This paper proposes a WL Hammerstein model and develops a robust recursive adaptive algorithm. • This paper also gives the simplified version of the proposed algorithm. • The performance analysis is derived and verified by simulations. • The superior behavior is demonstrated by example of system identification and SAEC. The Hammerstein model is a crucial model frequently employed in the field of signal processing. This paper focuses on the widely-linear (WL) complex-valued adaptive filtering and proposes a WL Hammerstein system model. Firstly, a cost function which minimizes the complex correntropic loss criterion is constructed. Subsequently, by extending the alternate direction method of multipliers (ADMM) optimization and non-convex projection method to the WL model, and leveraging the special structure of the autocorrelation matrix in the WL Hammerstein system, a robust recursive adaptive filtering algorithm for the WL Hammerstein system is proposed, along with its simplified version. More importantly, the performance analysis is studied, in which the convergence performance and the mean-square performance are provided. Simulations about system identification and stereophonic acoustic echo cancelation (SAEC) are carried out to validate the theoretical analysis and demonstrate the superior performance of the algorithms in this paper. [ABSTRACT FROM AUTHOR]

Thanks to the powerful modeling capabilities of multi-head self attention (MSA), transformers have shown significant performance gains in vision tasks. However, as transformers require heavy computation, more efficient designs are required. In this paper, we present an efficient transformer architecture named GoLDFormer for image restoration. GoLDFormer extends the capability of window-based self-attention through two core designs. First, We propose a globally-enhanced window-based transformer block (G-WTB), which applies transposed attention to a compressed window representation rather than the spatial features, thus establishing connections between all windows with less computational complexity. Second, since the interactions between image content and window attention weights can be interpreted as spatially varying convolution, we introduce an adaptive filter structure into transformer models and propose a deformable filtering block (DFB) to enable cross-window connections. By adjusting the shape of the generated filters in the DFB, we can balance the computational costs and the degree of adjacent window interaction. Extensive experiments on several image restoration tasks demonstrate that GoLDFormer achieves competitive results against recent methods but with optimal computational costs. • We propose GoLDFormer, including two blocks, to enhance W-MSA globally and locally. • Globally-enhanced attention block captures global dependencies between all windows. • Deformable filtering block captures local dependencies by modifying filter size. • Our GoLDFormer achieves competitive results against recent lightweight methods. [ABSTRACT FROM AUTHOR]

In this work, we study the combination of adaptive filters in colored noise environments. First, a combination framework using delayed weights is introduced to tackle the colored noise. Based on this, delayed convex and affine combinations of two LMS filters are developed, resulting in the so-called Dcvx-LMS and Daff-LMS algorithms. Then, the convergence behaviors of the two algorithms are investigated using standard mean-square deviation analysis. In addition, to speed up the convergence and reduce the computational complexity, we propose delayed combination with periodic feedback, delayed combined-step-size and block implementation methods. Finally, simulation results demonstrate the superiority of our algorithms over previously reported techniques in the presence of colored noise. [ABSTRACT FROM AUTHOR]