Linear Adaptive Filter Research Articles

Online bone/air-conducted speech fusion in the presence of strong narrowband noise

Online adaptive bone/air-conducted speech fusion is a very promising and attractive technique for real time speech communication in noisy environments. This paper firstly demonstrates that conventional bone/air-conducted speech fusion systems may degrade significantly, despite that only a tiny narrowband noise pollution persists in bone-conducted speech by extensive simulations and experiments. To address this issue, a robust nonlinear system is proposed that successfully enhances the speech contaminated by the harsh narrowband noise. The penetrated narrowband noise is extracted by an adaptive linear prediction filter from an extra reference signal that is capture by a non-contacted bone sensor, and then fed to the parallel adaptive noise cancellers to reconstruct a cleaner error feedback for the speech recovery system. Due to this combination, the proposed system enjoys both attractive robustness against the harsh additive noise and strong real-time capability. Simulations as well as application to a real factory noise are conducted to demonstrate the superior performance of the proposed system.

An adaptive fractional-order extended Kalman filtering approach for estimating state of charge of lithium-ion batteries

This paper proposes an adaptive fractional-order extended Kalman filter (AFEKF) approach for estimating state of charge (SOC) of a lithium-ion battery. Firstly, the fractional-order model (FOM) with a constant phase element module is established to describe the fractional-order characteristics inside a lithium-ion battery. Then, the augmented state equation including SOC is built by using the augmented vector approach. To avoid the calculation of the coefficients of the measurement equation, a linear adaptive integer-order Kalman filter is adopted. The AFEKF approach with the Sage-Husa filter is proposed to update the unknown parameters and unknown noises on the basis of augmented state equations. Finally, the comparison experiment between AFEKF approach and adaptive integer-order extended Kalman filter (AIEKF) approach is designed. Besides, the applicability of the AFEKF approach under different working conditions is also tested. The experimental tests indicate that the SOC estimation accuracy of the AFEKF approach is higher than that of the AIEKF approach, and the AFEKF approach can also be applicable in complex environments.

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch.

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.

Optimal Design of Hammerstein Cubic Spline Filter for Nonlinear System Modeling Based on Snake Optimizer

This work develops a new class of Hammerstein adaptive filters that contains a memory-less non-linear system followed by a linear adaptive filter, where the non-linear system comprises an adjustable look-up table (LUT) and a spline interpolator. This paper's first effort has been to employ the meta-heuristic algorithm (MHA) to the Hammerstein spline adaptive filter (HSAF), where it concurrently updates the weights of spline control points and linear filter based on the estimation problem. A novel MHA called snake optimiser algorithm (SOA) is used to enhance the assurance of convergence, estimated parameter accuracy and steady-state results. The presented experimental results indorse that the proposed SOA-based HSAF (SOA-HSAF) design exhibits more robust performance in dealing with higher degree non-linear systems under the Gaussian and Non-Gaussian circumstances compared to contemporary design methods like classical, some standard MHAs and other researchers reported techniques. The achieved simulation results are validated using a TMS320C6713 digital signal processor.

A Novel Kernel Algorithm for Finite Impulse Response Channel Identification


 
 
 
 Over the last few years, kernel adaptive filters have gained in importance as the kernel trick started to be used in classic linear adaptive filters in order to address various regression and time-series prediction issues in nonlinear environments.In this paper, we study a recursive method for identifying finite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive definite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the first of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm.
 
 
 

An adaptive phase‐locked loop‐less control strategy for LCL‐filtered grid‐connected inverter under complex grid conditions

SummaryThe phase‐locked loop, as a method to extract grid voltage phase angle, is commonly used for grid‐connected inverter to synchronize the injected grid current with the grid. However, the phase‐locked loop will also bring some problems. The current loop controller, the phase‐locked loop, and the grid impedance are coupled together under the weak grid. Then, the phase angle of the inverter output impedance will be reduced, causing the system to be unstable. Moreover, the output phase angle of the phase‐locked loop participates in the coordinate transformation under the unbalanced grid voltage condition, which degrades the quality of the injected grid current generated by the inverter with high harmonic content. Therefore, a vector current control strategy without phase‐locked loop derived from direct power control is used in this paper. An adaptive linear Kalman filter algorithm is also applied to observe the voltage information and avoid the harmonic problems caused by power calculation. Finally, simulations and experiments are conducted to verify that the proposed method is effective, and the results are consistent with the theoretical analysis.

On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model.

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An adaptive linear filter model of procedural category learning.

We use a feature-based association model to fit grouped and individual level category learning and transfer data. The model assumes that people use corrective feedback to learn individual feature to categorization-criterion correlations and combine those correlations additively to produce classifications. The model is an Adaptive Linear Filter (ALF) with logistic output function and Least Mean Squares learning algorithm. Categorization probabilities are computed by a logistic function. Our data span over 31 published data sets. Both at grouped and individual level analysis levels, the model performs remarkably well, accounting for large amounts of available variances. When fitted to grouped data, it outperforms alternative models. When fitted to individual level data, it is able to capture learning and transfer performance with high explained variances. Notably, the model achieves its fits with a very minimal number of free parameters. We discuss the ALF's advantages as a model of procedural categorization, in terms of its simplicity, its ability to capture empirical trends and its ability to solve challenges to other associative models. In particular, we discuss why the model is not equivalent to a prototype model, as previously thought.

A Robust Feedback Active Noise Control System With Online Secondary-Path Modeling

In this letter, a robust feedback active noise control (FBANC) system with an online secondary-path modeling (OSPM) subsystem is proposed for suppressing sinusoidal noise signals. First, a decoupling scheme is proposed by using of an adaptive linear prediction filter (LPF) that takes the FBANC residual noise as its input and separates the remaining sinusoidal noise from the broadband noise that includes an inherent additive noise within the primary noise as well as a broadband component due to an auxiliary white Gaussian noise (AWGN). The remaining sinusoidal noise (LPF output) is used effectively not only to update the FIR controller but also to scale the AWGN, while the broadband noise (LPF error) serves as a desired signal for the OSPM. In this way, the hostile coupling between the FIR controller and the OSPM subsystem is significantly mitigated, leading to both faster system convergence and improved noise reduction performance (NRP). Second, a simple and practical scheme for refreshing the proposed FBANC system is also developed to enhance its robustness against even abrupt changes with the SP and/or the primary noise signal. Extensive simulations with synthetic and real secondary paths and noise signals are conducted to demonstrate the effectiveness of the proposed FBANC system.

General Tensor Least-Mean-Squares Filter for Multi-Channel Multi-Relational Signals

Least-mean-squares (LMS) algorithms constitute a prevalent approach to implement the linear adaptive filters whose coefficients can be updated sample by sample so as to track time-varying dynamics. As the memory and computational complexities required for the realization of LMS filters are very low, they have been widely adopted in many real-time signal processing applications. The input of any conventional LMS filter has to be a sequence of scalar samples (one-dimensional time series), whereas such assumption is too restrictive nowadays for multi-channel (high-dimensional) signals and multi-relational data in the rise of a big-data era. It is crucial to deal with high-dimensional data-arrays, a.k.a. tensors, to manifest the variety and complex interrelations of data. Owing to lack of a sufficient mathematical framework to govern relevant tensor operations, the general tensor LMS filter, whose input is allowed to be an arbitrary tensor, has never been established for realization to the best of our knowledge. In this work, we will dedicate a new mathematical framework for tensors to establish the general tensor least-mean-squares (TLMS) filter theory and propose two novel TLMS algorithms with update rules based on stochastic gradient-descent and Newton's methods, respectively. Furthermore, as we establish the tensor calculus theory, the performance evaluation on convergence-rate and misadjustment for our proposed TLMS filters can be conducted. Finally, the memory and computational complexities of the new TLMS algorithms are also studied in this paper.

Maximum Total Improper Complex Correntropy Algorithm for Widely Linear Adaptive Filtering

The maximum total complex correntropy (MTCC) algorithm improves the performance of adaptive filtering under the error in variable (EIV) model by integrating both input and output noise information into the total complex correntropy. However, the MTCC algorithm cannot be applied to the widely linear model, directly. Compared with a strictly linear model, the widely linear model provides the complete statistical information of signals for adaptive filtering. This paper first constructs a novel cost function for the widely linear model by incorporating the input and output noise information into the improper complex correntropy. Then, a novel widely linear adaptive filter algorithm named widely linear maximum total improper complex correntropy (WL-MTICC) is proposed using the gradient descent method under the maximum total improper correntropy criterion. Finally, the analysis of local stability and convergence in the mean sense regarding the proposed WL-MTICC algorithm is provided. Simulations are used to show the performance advantage of the proposed WL-MTICC algorithm in the presence of both input and output noises.

Control of grid-connected residential solar-PV system using novel adaptive linear combiner filter for power quality improvement

ABSTRACT A grid-connected residential photovoltaic (PV) system has been proposed using novel adaptive linear combiner filter-based control for power quality (PQ) improvement. In the present work, novel adaptive linear combiner (ALC) filter-based control-based control has been proposed to generate reference current. MATLAB/Simulink software is used to develop the two-stage grid-connected residential PV. The proposed control is tested in MATLAB/Simulink2018(a) under nonlinear, linear load and variable input and validated experimentally on prototype hardware. The performance of the proposed ALC filter-based control has been analyzed and compared with that of conventional synchronous d-q control. It has been observed from results that THD in grid current using proposed control is 3.74% whereas using conventional d-q control THD is 4.75%. Proposed control provides more efficient performance by improving the PQ of the system by compensating the harmonics and reactive load demand. Harmonic distortion in the grid current is within the limits as per IEEE-1547. Furthermore, proposed control has better transient response viz. settling time, under shoot and overshoot in DC link voltage during grid connection of rooftop PV system and provides, reduced complexity due to absence of phase lock loop (PLL), thus less sampling time, better accuracy, ease of implementation, and adaptability.

Nonlinear residual echo suppression based on dual-stream DPRNN

The acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and the far-end signal. Usually, a post-processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of dual-path recurrent neural network (DPRNN) to improve the quality of speech communication. Both the residual signal and the auxiliary signal, the far-end signal or the output of the adaptive filter, obtained from the linear acoustic echo cancelation are adopted to form a dual-stream for the DPRNN. We validate the efficacy of the proposed method in the notoriously difficult double-talk situations and discuss the impact of different auxiliary signals on performance. We also compare the performance of the time domain and the time-frequency domain processing. Furthermore, we propose an efficient and applicable way to deploy our method to off-the-shelf loudspeakers by fine-tuning the pre-trained model with little recorded-echo data.

“Barcode” cell sensor microfluidic system: Rapid and sample-to-answer antimicrobial susceptibility testing applicable in resource-limited conditions

Many rapid antimicrobial susceptibility testing (AST) methods have been proposed to contain clinical antimicrobial resistance (AMR) and preserve the effectiveness of remaining antimicrobials. However, far fewer methods have been proposed to test AMR in resource-limited conditions, such as for frequent safety screenings of water/food/public facilities, urgent surveys of massive samples during a pandemic, or AMR tests in low-income countries. Rapid AST methods realized thus far have a variety of drawbacks when used for such surveys, e.g., high cost and the requirement of expensive instruments such as microscopy. A more reasonable strategy would be to screen samples via onsite testing first, and then send any sample suspected to contain AMR bacteria for advanced testing. Accordingly, a cost-efficient AST is demanded, which can rapidly process a large number of samples without using expensive equipment. To this end, current work demonstrates a novel “barcode” cell sensor based on an adaptive linear filter array as a fully automatic and microscope-free method for counting very small volumes of cells (~1.00 × 104 cells without pre-incubation), wherein suspended cells concentrate into microbars with length proportional to the number of cells. We combined this sensor with an on-chip culture approach we had demonstrated for rapid and automated drug exposure and realized a low-cost and resource-independent platform for portable AST, from which results can be obtained simply through a cell phone. This method has a much shorter turnaround time (2–3 h) than that of standard methods (16–24 h). Thanks to its microscopy-free analysis, affordability, portability, high throughput, and user-friendliness, our “barcode” AST system has the potential to fulfill the various demands of AST when advanced facilities are not available, making it a promising new tool in the fight against AMR.

Analysis of Normalized Orthogonal Gradient Adaptive Algorithm Based on Spline Adaptive Filtering for Smart Communication Technology

This paper presents a general theoretical framework of spline adaptive filtering based on a normalized version of orthogonal gradient adaptive algorithm. A nonlinear spline adaptive filter normally consists of a linear combination with a memory-less function and a spline function for adaptive approach. We explain how the adaptive linear filter and spline control points are derived in a straightforward iterative gradient-based method. In order to improve the convergence characteristics, the normalized version of orthogonal gradient adaptive algorithm is introduced by the orthogonal projection along with the gradient adaptive algorithm. In addition, a simple form of adaptation algorithm is introduced how to obtain a lower bound on the excess mean square error (MSE) in a theoretical basis. Convergence and stability analysis based on the MSE criterion are proven in terms of the excess MSE. Simulation results reveal that the proposed algorithm achieves more robustness compared with the conventional spline adaptive filtering algorithm.

Adaptive line enhancer for nonstationary harmonic noise reduction

In this paper, we propose a frequency-domain adaptive line enhancer (ALE) to reduce nonstationary harmonic noise, such as medical equipment beeps, from a noisy speech signal captured by a single microphone. The reduction of nonstationary noise is very challenging, with the tradeoff between noise reduction and speech distortion, often resulting with much noise residuals. The proposed ALE is a combination of the commonly-used forward adaptive linear filter and a non-causal backward adaptive linear filter used together with an indicator for the presence of transient noise. The proposed combined filter results in less noise residuals while preserving the speech components. We compare the proposed approach to conventional and recent methods, and show that it can outperform these methods, achieving lower distortion, more noise reduction, and overall better speech quality and intelligibility.

Normalized stochastic gradient descent learning of general complex‐valued models

The stochastic gradient descent (SGD) method is one of the most prominent first-order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes. It is applicable to a wide range of objective functions, while featuring low computational costs for online operation. However, without a suitable step-size normalisation, the convergence and tracking behaviour of the stochastic gradient descent method might be degraded in practical applications. In this letter, a novel general normalisation approach is provided for the learning of (non-)holomorphic models with multiple independent parameter sets. The advantages of the proposed method are demonstrated by means of a specific widely-linear estimation example.

Model Predictive Control and Adaptive Kalman Filter Design for an Underground Coal Gasification Process

The control of a nonlinear system such as an underground coal gasification (UCG) process is a challenging task. Several nonlinear design approaches are implemented to improve the tracking performance of the UCG process, however, the nonlinear techniques make implementation complex and computationally inefficient. In this work, a constrained linear model predictive control (MPC) is designed for the UCG process to track the desired trajectory of the heating value, while satisfying actuator constraints pertaining to UCG. The unknown states required for MPC design are reconstructed by using linear adaptive Kalman filter (AKF) and unscented Kalman filter (UKF). The design of MPC and AKF is based on the quasi-linear model of the UCG process. A fair comparison between different control strategies is conducted which include MPC– AKF, MPC– UKF, MPC– gain scheduled modified Utkin observer (GSMUO) and dynamic integral sliding mode control (DISMC)–GSMUO. The quantitative analysis and simulation results show that MPC- AKF outperforms its counterparts by yielding the least tracking error and average control energy. This conclusion holds, even in the presence of an external disturbance, parametric variations, and measurement and process noises. Moreover, MPC- AKF yields 51%, 44% and 46% improvement in absolute relative root-mean-squared error with reference to MPC– UKF, MPC– GSMUO and DISMC–GSMUO, respectively. A quantitative analysis has also been carried for AKF and UKF, which shows that the performance of AKF is more robust against changes in the initial values of measurement and process covariances.

A Distribution Independent Ship Detector for PolSAR Images

Ship detection with polarimetric synthetic aperture radar (PolSAR) images has attracted a lot of attention in recent years. However, modeling the distribution of clutter is a complicated task. This article introduces a distribution independent ship detector for PolSAR images. First, in order to improve the detection performance, the multichannel PolSAR data are projected onto a 1-D space utilizing an adaptive linear filter. The design of linear filter is modeled as a nonconvex optimization problem with the principle of maximization target-to-clutter ratio, which is solved by an iteration optimization algorithm. Then, the convergence and computational complexities of the proposed algorithm are theoretically analyzed. After that, a distribution independent detector with a bounded constant false alarm rate property is proposed to distinguish ships from sea clutter. The detection threshold is calculated based on the Markov inequality without modeling the statistical distribution of clutter. Experiments are carried out on real Radarsat-2 and AirSAR data to test the proposed detector. The results demonstrate that the proposed detector, which takes the distribution independent and unsupervised properties as the main advantages, also achieves comparable detection performance with state-of-the-art methods. Moreover, additional experiments verify the robustness of the proposed detector to the initialization of the algorithm, even though the optimization problem is nonconvex. Finally, the effects on detection results caused by polarization characteristics are investigated to give a further explanation about linear polarization enhancement.

A Collaborative Spline Adaptive Filter for Nonlinear Echo Cancellation

Acoustic echo causes the quality of communication to degenerate and results in loss of clarity. Though conventional linear adaptive filters have been applied successfully to eliminate linear acoustic echo, they can barely deal with the problem of nonlinear acoustic echo. In this paper, a collaborative spline adaptive filter is presented to eliminate nonlinear acoustic echo. The filter is composed of a linear adaptive filter in the upper branch and a nonlinear spline adaptive filter in the lower branch. The nonlinear one is a Hammerstein system consisting of a spline interpolation function and a subsequent linear adaptive filter. The two branches are collaboratively combined and a mixing parameter is adopted, which can be updated to adjust the proportion of the lower branch output signal. Experimental results show that the presented method can achieve a good performance in nonlinear echo cancelation regardless of whether the nonlinear degree of the echo path varies with time or not.