Performance Of Adaptive Filter Research Articles

A robust adaptive decomposable Volterra filter based on the hyperbolic tangent Leclerc function and its performance analysis

SummaryMost of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF‐DVM). The filter is implemented by imposing a rank‐one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady‐state mean‐square performance of the proposed HTLNAF‐DVM. Finally, the simulation results prove that the proposed HTLNAF‐DVM algorithm has better performance than the existing algorithms and fit well with the theory.

DsPIC Implementation and Performances Evaluation of Adaptive Filters for System Identification

This paper aims to implement and evaluate performances of adaptive filters applied to linear systems identification on a dsPIC platform. To do so, we considered two different applications: the identification of DC motor transfer function and the acoustic echo cancellation. For the DC motor case, real-time identification is not necessarily required. However, for the acoustic echo cancellation case, real-time identification is crucial, making execution time a key parameter in this scenario. We have implemented three adaptive algorithms: LMS, NLMS, and RLS. The first two are known for their computational simplicity, while RLS is recognized for its convergence speed. Performances evaluation is primarily based on accuracy and computation speed. A comparison is carried out in both cases to determine which algorithm is more suitable for each application. The results obtained showed that RLS is better suited for the DC motor case, while NLMS is more suitable for acoustic echo cancellation, particularly when the impulse response of the echo path is long.

A comparative study of accuracy in major adaptive filters for motion artifact removal in sleep apnea tests.

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.

Rational b-spline adaptive filter for active noise control of machinary noises

Spline adaptive filter has a different architecture than linear-in-parameter nonlinear filters like Volterra filter. There are various kinds of splines like Bezier spline, B-spline and rational B-spline. Rational B spline has weights associated with control points and hence, provides more flexibility than B-spline to create a curve. This paper presents development of rational b-spline adaptive filter for active noise control. Updating equations to update control points and weights of control points are derived. A simulation study is presented to study performance of proposed rational B-spline adaptive filter to reduce machinary noises. Performance of proposed rational B-spline filter are compared with B-spline filter and Bezier filter for active noise control of experimentally recorded noises of band-saw, CNC machine and compressor

Adaptive block level bilateral filtering algorithm

During the acquisition or transmission process, video images are subject to random signal interference and generate noise, which can hinder people's understanding of the image and subsequent processing work. Therefore, it is necessary to study video image denoising and filtering algorithms. Bilateral filter is one of many typical video image filtering algorithms. However, the traditional bilateral filter algorithm does not consider the differences in the contents of different regions of the image. It is difficult to obtain the optimal filtering effect by using a fixed filtering weight to filter the entire image, which leads to problems such as blurred image edges and inadequate details processing. This paper studies the influence of different filter block sizes on the bilateral filter effect, and proposes an algorithm to adaptively update the bilateral filter weight according to the block's variance. The experimental result shows that the performance of adaptive bilateral filter with different block sizes is expected to be better than that of traditional algorithms with fixed filter weights.

Adaptive unscented Kalman filter for neuronal state and parameter estimation.

Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter implementations, the robust adaptive unscented Kalman filter (RAUKF) jointly estimates the states and parameters of the neuronal model while adjusting noise covariance matrices online based on innovation and residual information. We benchmark the adaptive filter's performance against existing nonlinear Kalman filters and explore the sensitivity of the filter parameters to the system being modelled. To evaluate the robustness of the proposed solution, we simulate practical settings that challenge tracking performance, such as a model mismatch and measurement faults. Compared to standard variants of the Kalman filter the adaptive variant implemented here is more accurate and robust to faults.

An Efficient Filtering Algorithm against Impulse Noise in Communication Systems

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

Robust Subband Adaptive Filter Algorithms-Based Mixture Correntropy and Application to Acoustic Echo Cancellation

To acquire an improvement of the performance of the subband adaptive filter with impulsive interference, the normalized subband adaptive filter (NSAF) algorithm-based maximum correntropy criterion (MCC), called MCC-NSAF, has been developed. However, it is hard for the MCC criterion to take into account both the fast convergence rate and low steady-state error with a fixed kernel bandwidth. To handle this dilemma, in this paper, the normalized subband adaptive filter algorithm-based maximum mixture correntropy criterion (MMC) algorithm, called MMC-NSAF, is proposed. The MMC-NSAF algorithm integrates two correntropies with complementary control sizes of Gaussian kernel and can not only converge quickly but also has a small misalignment. Yet the MMC-NSAF still has some limitations because of its fixed step size. Therefore, a convexly combined MMC-NSAF algorithm, namely MMC-CNSAF, is proposed. The MMC-CNSAF algorithm utilizes two Gaussian kernels of different size and can combine overall improvement between convergence rate and steady-state error simultaneously. Eventually, the convergence and the computational complexity are analyzed. To achieve the test and verification of the robustness of proposed algorithms, simulations are carried out to discuss the effect of the parameters algorithm under colored input signals in impulsive interference environments for system identification. Meanwhile, the performances of the proposed algorithms under the influence of the acoustic echo cancellation in practical application are studied.

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

This paper describes a unique study that uses multiple FIR adaptive filter algorithms to denoise adult electrocardiogram (ECG) data. The study looks at how power line interference, external electromagnetic fields, random body motions, and breathing impact ECG measurement accuracy. The article takes a fresh look at Savitzky-Golay filtering techniques by implementing and evaluating them inside the FIR adaptive filter architecture. Matlab is used to evaluate the performance of the Affine projection FIR adaptive filter (AP), Direct-form Normalized least-mean-square FIR adaptive filter (NLMS), and Sliding-window Recursive least-squares FIR adaptive filter (SWRLS). The results show how different strategies compare in terms of performance and their influence on recorded waveform quality. The study extends to our understanding of the efficiency of FIR adaptive filter algorithms in decreasing ECG signal noise and helps us better understand their potential uses in ECG signal processing. Based on reliable ECG data, the research findings assist the development of new approaches for diagnosing aberrant cardiac rhythms and examining the origins of chest discomfort. The originality of this work comes in its thorough assessment, comparison, and unique use of Savitzky-Golay filtering techniques inside FIR adaptive filter algorithms, which contributes to the area of ECG signal denoising. According to a comparative investigation, the SWRLS FIR adaptive filter method improves ECG signal denoising by 91.53% noise reduction.

Performance analysis of the augmented complex-valued least mean kurtosis algorithm

The augmented complex-valued least mean kurtosis (ACLMK) algorithm was proposed to enhance the performance of adaptive filters by utilizing the negated kurtosis of the error signal. This paper provides a comprehensive physical insight into its stochastic behaviors and a more thorough performance analysis. By using Isserlis’ theorem, the evolutions of both the covariance and complementary covariance matrices of the weight error vector are established, based on which the expressions for transient mean-square deviation (MSD) and excess mean-square error (EMSE) are derived. Moreover, the higher-order moments of the measurement noise are also taken into account. Different from the existing framework which treats the ACLMK as a variable step-size augmented complex-valued least mean square (ACLMS) algorithm, the steady-state mean-square performance of the ACLMK is analyzed through the approximate uncorrelating transform (AUT) to yield the theoretical steady-state MSD and EMSE. The proposed framework of performance analysis is shown to provide more accurate models for the theoretical MSD and MSE. Simulation results are provided to validate the theoretical findings for second-order noncircular input signals in second-order circular and noncircular noisy environments.

Robust adaptive filtering algorithms based on the half‐quadratic criterion

Recently, robust adaptive filtering algorithms have attracted the interest of researchers and have been extensively studied. However, most of these methods suffer from the non-convexity of their performance surfaces, which results in reduced convergence rate and filtering accuracy. To address this problem, we present a novel robust half-quadratic criterion (HQC) adaptive filtering algorithm by utilizing a convex cost function. In contrast with conventional methods, the proposed HQC can introduce a more effective performance surface, allowing a gradient-based strategy to provide significant performance improvements in convergence speed and robustness against impulsive noise. Furthermore, to enhance the performance of the HQC adaptive filter, a variable step-size (VSS) version, the VSS-HQC algorithm, has also been introduced. Using the energy conservation method, the theoretical expressions for the transient, steady-state, and tracking performance analysis of the proposed HQC algorithm are studied under the Gaussian noise assumption. Then, we examined the theoretical analysis of the HQC using various numerical simulations for system identification to validate the findings. Finally, we compared the performance of the HQC and VSS-HQC algorithms to their competitors in both Gaussian noise and non-Gaussian noise scenarios through numerical experiments.

Complex-Valued Random Fourier Geometric Algebra Adaptive Filtering

The complex-valued fixed dimensional adaptive filters (CFDAFs) can improve the performance and computational efficiency of complex-valued kernel adaptive filters (CKAFs), but have the challenge of over-coupling for the real and imaginary parts of complex-valued signals. To this end, this brief proposes a novel fixed dimensional adaptive filter for complex-valued signals named complex-valued random Fourier geometric algebra least mean square (CRFGALMS). On the basis of the framework of geometric algebraic adaptive filtering, the real and imaginary parts of complex-valued signals are mapped into random Fourier features space (RFFS) to improve the efficiency of the nonlinear mapping for complex-valued signals, respectively. The proposed random Fourier feature mapping based on the geometric algebra is endowed with superior presentation abilities in complex-valued domain as it can decouple the nonlinear mapping of the real and imaginary parts of complex-valued signals, efficiently. Simulations on nonlinear channel equalization are used to illustrate the superiority of the proposed CRFGALMS algorithm in terms of dimensionality and filtering performance.

A Novel Family of Sparsity-Aware Robust Adaptive Filters Based on a Logistic Distance Metric

In the recent past, logarithmic hyperbolic cosine based-cost function has been widely applied in adaptive filters as it offers robust performance against outliers. However, the performance of such adaptive filters suffers from high steady-state misalignment due to its significant weight update, even in the presence of outliers. This paper proposes a logistic distance metric-based novel robust cost function, and the corresponding logistic distance metric adaptive filter (LDMAF) has been developed. The proposed LDMAF provides negligible weight update when the desired signals are affected by significant outliers, resulting in low steady-state misalignment. The bound on learning rate has been estimated, and computational complexity comparison of the proposed and other existing algorithms has also been carried out. To further exploit the system's sparse nature and robustness against the outliers, zero attraction-based LDMAF (ZA-LDMAF) and re-weighted zero attraction-based LDMAF (RZA-LDMAF) algorithms have also been developed in this paper. In addition, a new sparse penalty function based on a generalized multivariate Geman-McClure function has been introduced, which provides smooth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula> -norm approximation over other existing functions. Based on this new sparse penalty function, this paper has also developed a novel sparsity-aware robust adaptive filter called generalized Geman-McClure LDMAF (GGM-LDMAF). Simulation studies confirmed the improved convergence behaviour achieved by the proposed algorithms over other existing algorithms for system identification and acoustic echo cancellation scenarios.

VLSI Implementation of DENLMS Adaptive Filter for Biomedical Applications

Very Large Scale Integration (VLSI) implementation of the Delayed Error Normalized LMS (DENLMS) adaptive filter using a pipelined architecture is proposed and it can be used for biomedical applications. The proposed pipelined VLSI architecture increases the performance of adaptive filters by lowering the amount of time spent on critical path calculation. When compared to the current Error Normalized Least Mean Square (ENLMS) method for pipelining purposes, the DENLMS technique requires an additional delay element, and because of the delay element, the algorithm functions faster and more effective in low-power applications. The proposed pipelined architecture of the DENLMS filter, on the other hand, improves the efficiency while consuming less power overall. The cell leakage power reduction and total dynamic power reduction obtained by using the DENLMS filter are 31.8% and 33.5%, respectively, although the overall area of the filter has increased by 20.4%.

Learning and Application of Feedback Path Database for Feedback Control in Hearing Aids

State-of-the-art feedback control systems make use of adaptive filters to model and compensate for the acoustic feedback paths, which vary in different acoustic situations. In a hearing aid application, the acoustic feedback paths are user dependent and vary over time. Although there are repeatable patterns in these variations, it remains to be seen that modern feedback control systems actively learn from and then use these patterns to improve the adaptive filter performance. In this letter, we present a new idea of learning and storing acoustic feedback paths as hearing aid users wear their hearing aids, and then to apply these learned and stored acoustic feedback paths to control and support the adaptive filter updates, thereby to achieve significantly improved feedback control performance.

Multirate Adaptive Equalization

Finite Impulse Response (FIR) filter model emulates the Inter Symbol Interference (ISI) in a wireless communication channel. An equalizer, typically an Infinite Impulse Response (IIR) filter, behaves as an inverse filter to the FIR filter to remove the effects of the ISI. IIR filters are generally avoided due to tractability issues, and an FIR filter, with an adaptive signal processing algorithm to minimize the error due to the ISI, is deployed at the receiver. However, the filter is observed to quickly reach a steady state where further iterations do not yield a reduction in the error. This can be attributed to relatively slow variations in the steady state error which prevent further reduction of the errors. This work focuses on converting the low frequency error variations to high frequency variations by the use of multirate signal processing. As such, the steady state error can be damped as well, providing further reduction in the error and an enhanced adaptive filter performance.

Robust and Sparse Aware Diffusion Adaptive Algorithms for Distributed Estimation

In distributed wireless sensor networks, geographically distributed sensors cooperate wirelessly with each other. While sensing from the environment, the signals from these sensors are often contaminated by noise. Traditional diffusion algorithms for distributed estimation consider this noise to be Gaussian in nature. However, in practice this noise can also be non-Gaussian, which leads to deterioration in performance of traditional adaptive algorithms. Moreover, the parameter vector to be estimated may be sparse in nature. To improve adaptive filter performance for distributed networks, we propose a set of sparsity aware diffusion adaptive filters which are robust to non-Gaussian noises. Extensive simulation study for different Gaussian and non-Gaussian noise environments show the improved estimation ability of the proposed algorithms for modelling highly, moderate and non-sparse distributed systems.

A Dual Adaptive Filter Spike-Based Hardware Architecture for Implementation of a New Active Noise Control Structure

Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.

A family of logarithmic hyperbolic cosine spline nonlinear adaptive filters

Spline nonlinear adaptive filters are well known for their ability to efficiently model nonlinear systems while having low computational complexity. However, the performance of traditional spline adaptive filter degrades in the presence of impulsive disturbances. For improving the performance of spline adaptive filters in impulsive noise scenarios, a logarithmic hyperbolic cosine spline adaptive filter is proposed in this paper. To improve the convergence rate while maintaining low steady-state error, a variable parameter approach for the logarithmic hyperbolic cosine spline adaptive filter is also proposed. In addition, a robust nonlinear active noise control approach is also designed based on the proposed logarithmic hyperbolic cosine spline adaptive filter. The computational complexity of the proposed algorithms is studied and two approximate versions of the logarithmic hyperbolic cosine spline adaptive filter are proposed to decrease computational cost without degrading performance. Bound on learning rates are also derived to ensure the stability of the proposed adaptive systems. The simulation studies conducted, demonstrate the enhanced performance achieved by the proposed algorithms.

Power Quality Issues Compensation using ANN Techniques

This idea is to propose an active filter which adopts to power quality issues and to solve this through Artificial intelligence techniques. This proposed active filter is combined to shunt active filter (SAF) which is controlled by neuro fuzzy interference system (ANFIS). This proposed adaptive filter performance is enhanced by ANFIS tool. This ANFIS is a class of adaptive system these functions are equivalent to fuzzy based systems. The purpose of ANFIS is to control the discharging time of dc-link capacitor in SAF there by reduces the power quality issues. The dc-link voltage values are get affected by the load fluctuation and these fluctuations can be suppressed by measuring and regulating the reference voltage. The purpose of ANFIS base adaptive shunt active filter is to achieve enhanced power quality there by reducing dc link voltage and current fluctuations very quickly. The proposed technique implemented and results are obtained using MATLAB its performance is evaluated through reference value.