Application Of Adaptive Filter Research Articles

Advancements and Applications of Adaptive Filters in Signal Processing

Advancements and Applications of Adaptive Filters in Signal Processing

Robust constrained cosine arctangent based adaptive filtering for feedback control in hearing aids

The striking problem of acoustic feedback remains a persistent challenge in hearing aids (HAs), imposing limitations on attainable amplification and significantly deteriorating sound quality, often manifesting as disruptive howling artifacts. With the objective of feedback mitigation, the feedback path is estimated using an adaptive filter. The constrained least mean square (CLMS) technique is commonly employed in adaptive filtering applications where it is necessary to meet specific linear constraints. However, algorithm's robustness is compromised when impulsive or non-Gaussian noise interference occurs. In lieu of this, a novel algorithm known as the robust constrained cosine arctangent (CCAT) adaptive filtering is introduced to enhance the convergence behavior. This has been formulated with the inclusion of linear constraints to the suggested cosine function integrated with the arctangent technique. This algorithm aims to address the issue of feedback cancellation in hearing aids along a set of constraints. To achieve a minimal steady-state error and expedite the convergence of the method, a novel policy that adjusts the step factor has been suggested. CCAT-VSS is a modified version of CCAT that includes a variable step size (VSS). The simulation findings demonstrate that the suggested method exhibits superior performance for misalignment, added stable gain and certain voice quality assessments.

An experimental comparative study of adaptive sigma-point Kalman filters: Case study of a rigid–flexible four-bar linkage mechanism and a servo-hydraulic actuator

This paper investigates the application, design, and implementation of different adaptive sigma-point Kalman filters for nonlinear dynamic systems. It is a well-known fact that the performance of nonlinear Kalman filters depends on a prior knowledge of system noise statistics. In real-time applications, this knowledge is difficult and in some cases impossible to predefine. Therefore, an adaptation mechanism based on the random weighting method is proposed in this work to estimate, simultaneously, noise covariance and the state of the system. On the other hand, for real time execution, the computational efficiency of such filters should also be considered to minimize the burden on computational resources. Based on these two factors, in this paper, four different variants of the adaptive sigma-point Kalman filter are studied in detail: (I) an Adaptive Unscented Kalman filter (AUKF), (II) an Adaptive Square-root Unscented Kalman filter (ASr-UKF) with an Unscented Transformation sigma-point selection strategy, (III) an Adaptive Scaled Spherical Simplex Unscented Kalman filter (AS3UKF), and (IV) an Adaptive Square-root Scaled Spherical Simplex Unscented Kalman filter (ASr-S3UKF) with scaled spherical simplex sigma-point selection strategy. The performance of these adaptive sigma-point Kalman filters is analyzed here in depth and discussed for application to a simulated rigid–flexible four-bar linkage mechanism and a practical servo-hydraulic actuator setup under different scenarios. The simulation and the experimental results indicated that, for the cases studied in this article, the filters are competitive and present advantages and disadvantages that should be dealt with according to the requirements of the problem.

Neural network generation of adaptive filter and new applications in remote sensing image processing

Neural network generation of adaptive filter and new applications in remote sensing image processing

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.

Joint application of hybrid iterative reconstruction and adaptive filters on neck-and-shoulder CT imaging: A clinical evaluation.

To assess whether the joint application of hybrid iterative reconstruction (HIR) and an adaptive filter (AF) could reduce streak artifacts and improve image quality of neck-and-shoulder computed tomography (CT). This study included 96 patients with suspicious neck lesions who underwent a routine nonenhanced scan on a 64-slice CT scanner. The raw data were reconstructed using four different settings: filtered back projection (FBP), HIR, FBP+AF, and HIR+AF. Regions of interest were manually drawn in erector spine, axillary fat, latissimus dorsi, and dorsal cervical fat. Mean and standard deviation (SD) of the CT number, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained and compared using Wilcoxon signed-rank tests. The qualitative assessments of five factors were compared by two independent investigators. Compared to the other three settings, HIR+AF reduced noise in the area where the streak artifact of the lower neck were most serious (SD; all p≤0.001). The SNR and CNR were improved significantly (all p≤0.001). Compared to the other three settings, HIR+AF showed a significant improvement in CT image quality regarding the visibility of suspicious lesions, the extent of streaking artifacts, noise, soft-tissue contrast, and visualization of small structures (all p≤0.02). The combination of HIR and AF can significantly reduce streaking artifacts and improve image quality in neck-and-shoulder CT imaging.

A simplified design of a cEEGrid ear-electrode adapter for the OpenBCI biosensing platform

We present a simplified design of an ear-centered sensing system built around the OpenBCI Cyton & Daisy biosignal amplifiers and the flex-printed cEEGrid ear-EEG electrodes. This design reduces the number of components that need to be sourced, reduces mechanical artefacts on the recording data through better cable placement, and simplifies the assembly. Besides describing how to replicate and use the system, we highlight promising application scenarios, particularly the observation of large-amplitude activity patterns (e.g., facial muscle activities) and frequency-band neural activity (e.g., alpha and beta band power modulations for mental workload detection). Further, examples for common measurement artefacts and methods for removing them are provided, introducing a prototypical application of adaptive filters to this system. Lastly, as a promising use case, we present findings from a single-user study that highlights the system's capability of detecting jaw clenching events robustly when contrasted with 26 other facial activities. Thereby, the system could, for instance, be used to devise applications that reduce pathological jaw clenching and teeth grinding (bruxism). These findings underline that the system represents a valuable prototyping platform for advancing ear-based electrophysiological sensing systems and a low-cost alternative to current commercial alternatives.

Simulation of LMS based adaptive noise cancellation using Labview

The audio signals processed in the signal measurement systems are inevitably susceptible to unwanted noise which significantly affects the quality of the signal and the overall performance of the signal communication systems. Due to its' random and unpredictable nature, the amount of noise in signals has proven to be a significant issue in designing these systems and recently has been a trending research topic. In this regard, the active noise cancellation method has proven to be an effective technique for eliminating the noise effects on signal processing. The concept of active noise cancellation is based on the application of adaptive filters and algorithms proposed to reduce the signal corruption and distortion caused by the noise due to the principle of destructive interference. In this paper a simulation model of active noise reduction technique using the LMS (Least Mean Square) algorithm in Labview is presented. The purpose of the work is to investigate the noise cancellation effect on a recorded audio file in terms of analyzing the audio file before and after filtering out the noise by using the LMS algorithm and discuss the results thereof.

Fractional‐order complex correntropy algorithm for adaptive filtering in α‐stable environment

In adaptive filtering applications, the Gaussian distribution cannot be used to model the signal/noise with frequent spikes accurately. In fact, the rational model to simulate the behaviour of such signal/noise is the α-stable distribution process. In this letter, a fractional-order complex correntropy algorithm is proposed to deal with the case that both signal and noise processes are modelled as complex-valued α-stable signals. Compared with the classical approaches, the proposed fractional-order complex correntropy extends the Gaussian assumption of signal/noise in the complex domain to the assumption of α-stable distributions without second-order and higher order statistical moments. Benefitting from the fractional-order calculus and correntropy criterion, fractional-order complex correntropy shows great robustness to the jittery behaviour of complex-valued α-stable signal/noise. In addition, a convergence analysis for fractional-order complex correntropy has been carried out. Simulations on system identification revealed that the filtering performance is significantly improved by using fractional-order complex correntropy.

A study on non-linear discrete-time state-space models and adaptive extended Kalman filter application on oscillatıon of an object tied to the end of spring

A study on non-linear discrete-time state-space models and adaptive extended Kalman filter application on oscillatıon of an object tied to the end of spring

Selective partial-update augmented complex-valued LMS algorithm and its performance analysis

Adaptive filtering applications such as acoustic echo cancellation may require a large number of filter weights in order to estimate the unknown system with a long response, leading to high computational complexity. Using partial-update techniques is an effective method to alleviate this issue, and two partial-update augmented complex-valued LMS (PU-ACLMS) algorithms have been recently proposed. However, their convergence rates severely deteriorate due to using a stochastic or sequential weight update manner. This paper proposes a new PU-ACLMS, which selects the weights corresponding to the M largest moduli of input samples to update, with M less than the number of all weights. The mean and mean-square performance of the proposed algorithm is then analyzed to predict its stochastic behavior. In order to achieve this goal, the values of two scale factors are derived by using multiple integrals, which play an important role in our performance analysis. Finally, the performance of the proposed algorithm is compared with that of the sequential PU-ACLMS and stochastic PU-ACLMS, and the theoretical findings are verified by extensive simulations.

Hybrid PSO-NLMS (HPSO-NLMS) algorithm for blind speech quality enhancement in time domain

Speech intelligibility enhancement by adaptive two-channel filtering algorithms in the time domain is a fundamental problem in the areas of signal processing application. In this paper, we address this problem and suggest a new adaptive hybrid algorithm combined with Forward blind source separation (FBSS) structure to retrieve a speech signal from noisy observations. Usually, the Normalized Least Mean Square algorithm (NLMS) is widely used in adaptive filtering application, however conventional NLMS presents difficulties to solve the trade-off between the speed of convergence and the low steady state error. Recently, numerous variable step-size NLMS (VNLMS) algorithms were proposed to solve this trade-off, our proposed algorithm falls within the same type of algorithm. The proposed Hybrid PSO-NLMS (HPSO-NLMS) algorithm is a combination of the NLMS and the Particle Swarm Optimization algorithms (PSO), where we use PSO to obtain the appropriate NMLS step size at each iteration. We integrate the new HPSO-NLMS in the FBSS structure, and compare their performances to conventional NLMS and PSO algorithms. We show that the proposed algorithm can produce better noise reduction performance, in terms of several objective metric such as the system misalignment (SM), the Segmental signal to noise Ratio (SegSNR) and the segmented mean square error (SegMSE).

A Computationally Efficient Variational Adaptive Kalman Filter for Transfer Alignment

To better solve the filtering problem of transfer alignment with an inaccurate measurement noise covariance matrix, a novel computationally efficient version of existing variational adaptive Kalman filter is proposed in this paper, in which an equivalent variational iteration process of the measurement noise covariance matrix is derived. The proposed filter is identical to the existing variational adaptive Kalman filter, but the total computational complexity of algorithm implementation is significantly reduced, which facilitates the application of variational adaptive Kalman filter to transfer alignment. Simulation and experiment results of transfer alignment demonstrate that the computational complexity of the proposed filter is reduced by 55.4% as compared with existing variational adaptive Kalman filter.

Power-Flow-Based Stabilization for Adaptive Feedforward Filters in Hybrid Testing

Real-time hybrid testing is a technology which allows the coupling of simulations and component tests in order to simulate complex system dynamics. Delays and time lags caused by actuator dynamics and signal processing deteriorate the stability of the tests in many cases. The application of adaptive feedforward filters to hybrid testing enables circumventing this problem. The stability of the filter itself, however, can be affected by the choice of the algorithm parameters or changes in dynamics of the system being tested. Test safety requirements and practical considerations require a failsafe implementation. In this paper, we propose a method for adjusting the parameters of the adaptive feedforward filter based on power-flows in the test setup. The objective is to maintain a passive behavior of the actuation and control system. The stabilization acts on the leakage factor and the adaptation gain of a least-mean-squares adaptation law. A simple numerical system is used to investigate the effect of the algorithm parameters on the stabilization. The method was applied to an experimental setup including a nonlinear stiffness. Several originally unstable configurations were stabilized, the adaptation process could be continued and interface synchronization was achieved in all test cases.

Real Time Implementation of SIGN LMS Adaptive Filters using Xilinx System Generator

Sign Least Mean Square (SLMS) adaptive filter can adapt dynamically based on corresponding filter output. One of the major applications of adaptive filter is Noise cancellation. In real time applications like medical computing, speed of the process developing hardware is essential hence the hardware realization of SLMS adaptive filter using Xilinx System generator is proposed in this work. The propose architecture aims to reduce convergence rate, path delay and increasing speed. In this work (i) Modified architecture is designed for a 8-tap SLMS adaptive filter and (ii) multiplier less structure for Modified DLMS Filter. The designed architecture tested for ECG signal. The functionality of the algorithm is verified in MATLAB with various ECG data from the MIT-BIH database as input. Both LMS and SLMS are designed, simulated, synthesized and implemented in Virtex-5 FPGA using Xilnix ISE 14.3 . The result shows 5% decrease in total real time router completion and also decrease in the number of adders and subtractors, the maximum combinational path delay has been reduced by 48.84% in Systolic Sign LMS Filter when compared to LMS Filter.

Fast and robust Fourier domain ranging for light field

A light-field camera combines optics and computation to provide the ability to perform ranging. Many methods and algorithms, which are time-consuming and noise-sensitive, have been proposed for per-pixel depth estimation. We present a Fourier domain ranging method for light fields (LFs). Instead of estimating the depth for every pixel, we attempt to detect the depths of the different planes at which objects are located. The method is somewhat like using the energy in the focal stack, but it is carried out in a more efficient way. Our method has the advantages of speediness and robustness compared with traditional per-pixel depth estimation methods. In addition to the ranging algorithm, we also demonstrate the idea and application of a region adaptive denoising filter, in which the depth parameters are tuned by the proposed method. We include results for synthetic LFs, the Stanford LF archives, and LFs captured with a Lytro camera, exploiting the algorithm complexity, accuracy, depth resolution, and noise performance of our method. Our method uses less computation and is more robust than per-pixel depth estimation, making it appropriate for many applications, such as autorefocusing and autodenoising.

Application of adaptive filters to improve online detection sensitivity of 13CO2 in a mixture with 12CO2 in expiratory air

The use of adaptive Kalman and Wiener filters and the empirical mode decomposition algorithm for processing the experimental signal obtained by the tunable diode laser absorption spectroscopy method has been reported to improve the sensitivity of the measurement of 13CO2 content in human expiratory air. A laser diode, tunable in the range 4860–4880 cm−1 near the wavelength of 2 µm, is used as a source. It has shown that the application of these filters ensures a detection sensitivity of 13CO2 at the level of 1015 cm−3, while the minimum detectable fraction of 13CO2 in a mixture with the 12CO2 is 3.8 · 10−5.

Application of adaptive square root cubature Kalman filter in turbofan engine gas path performance monitoring

Kalman filters are very popular in turbofan engine community for health monitoring purposes. In this study, in order to get better performance in terms of filtering accuracy and noise adaptability, an Adaptive Square Root Cubature Kalman Filter (ASRCKF) was proposed to estimate health parameters of the turbofan engine gas path components. In the ASRCKF algorithm, the mean value and covariance of the engine nonlinear function were calculated by cubature rule-based numerical integration method and used as a substitute for nonlinear model in nonlinear Kalman filter. The latest information of measurement parameters in the recursion and filtering process was used to estimate and self-adjust the noises cross-covariance by removable window method to get higher filtering accuracy. Compared with the Extended Kalman Filter (EKF) and Square Root Cubature Kalman Filter (SRCKF), the simulation results in the gradual and rapid deterioration process of turbofan engine gas path components indicate that the higher accuracy and faster convergence can be obtained through ASRUKF, which can be used in health parameters estimation and condition monitoring of turbofan engine gas path.1

Пригнічення акустичних перешкод у системах дистанційного моніторингу атмосфери з використанням ґратчастих фільтрів

The questions related to the choice of effective frequency of sounding signal in the acoustic sounding systems of the ground layers of atmosphere in the district of airport are investigated and presented. For an achievement of big heights it is necessary that the sounding signal frequency does not exceed 1 kHz. However in this range the noise level from airliners is high in an airport. To provide flight safety in an airport it is impotent to know the wind parameters on small heights, therefore researches are fulfiled for sounding signal frequencies of 3 kHz and 5 kHz. The parameter evaluation accuracy of the sounding acoustic signal reflected from an atmosphere inhomogeneity is defined by the statistical simulation technique with the adaptive suppression process of the airliners interference. The conducted experiments show that for these sounding signals frequencies the estimation accuracy of the reflected signal frequency depends negligibly on the chosen frequency. So, the sounding signal frequency may be changed in a wide range during sounding system operation near-by an airport, with taking into account descriptions of interference spectrums. Experiments were conducted for the acoustic interference created by the airliners Boeing 707 and Boeing 737. It is shown also, that application of the adaptive lattice filters and adaptive filters with the finite impulse response (FIR) for suppression of the acoustic interference created by airliners in airports substantially brings down the interference influence on the estimation accuracy of a wind speed measured by acoustic sounding systems. At weak acoustic interference and noise level application of the adaptive lattice filters and filters with FIR gives approximately identical results, and at considerable noise levels the use of the latticed filters is more preferable. The research results comport with conclusions on the practical application of adaptive lattice filters and linear prediction filters with FIR.

Development of an Adaptive IIR Filter Based on Modified Robust Mixed-Norm Algorithm for Adaptive Noise Cancellation

Noise cancellation is one of the most important applications of adaptive filters. The employment of adaptive filtering in most digital signal processing tasks is currently an area of growing interest as adaptive filters, due to their dynamic nature, perform better than the traditional filters in compensating for random noise in their environment. However, the compensation for impulsive interference or noise is desired since most adaptive algorithms earlier proposed modelled noise as a random process of the White Gaussian distribution. A modified robust mixed-norm (MRMN) algorithm recently proposed to compensate for impulsive interference has been found to be hardware efficient, however the MRMN algorithm has only been tested on adaptive FIR system identification task. In this paper, an adaptive IIR filter based on MRMN adaptive algorithm is proposed and tested for noise cancellation task. The developed filter structure was modelled and simulated in MATLAB environment. The results obtained showed that the MRMN algorithm does in fact compensate for the presence of impulsive interference, however, at a higher computational complexity relative to the LMS algorithm. Keywords: Noise cancellation, adaptive filtering, impulsive noise, adaptive algorithm, system identification, random noise DOI : 10.7176/CEIS/10-2-01 Publication date :March 31 st 2019