Showing papers on "Adaptive filter published in 2022"

Optimal Adaptive Filtering Algorithm by Using the Fractional-Order Derivative

Abstract: The previous work for the filter design considers uncorrelated white measurement noise disturbance. For more complex correlated noise disturbance, the conventional adaptive filter results in biased estimates. To overcome this problem, we introduce a linear prefilter to whiten the correlated noise (i.e., colored noise) for obtaining the unbiased estimate of the filter weight. Moreover, the design of some adaptive filters mainly focuses on the integer-order optimization methods. However, compared with the integer-order-based adaptive algorithms, the fractional-order-based algorithms show better performance. Thus, this letter develops a new gradient approach for the adaptive filter design based on the fractional-order derivative and a linear filter. Finally, the simulation results are provided from the system identification perspective for demonstrating the performance analysis of the proposed algorithms.

Multichannel adaptive signal detection: basic theory and literature review

Abstract: Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks.

Modified Proportionate Affine Projection Algorithm Based Adaptive DSTATCOM Control With Increased Convergence Speed

Abstract: The three-phase DSTATCOM is prone to higher dynamics due to grid disturbances. The proportionate affine projection algorithm (PAPA) is an adaptive filter that can be used for DSTATCOM control. In order to overcome the sluggish convergence speed of adaptive filters, PAPA is proposed in this paper. The convergence rate versus the steady-state error is a trade-off in conventional adaptive filters. However, the utilization of two adaptive filters in CSS-PAPA increases the convergence and decreases the steady-state error. The suggested filter has the advantage of having a lower computational cost than a standard adaptive filter. The proposed filter helps the inverter to work as a shunt compensator. The goal of the suggested controller is to adjust for reactive power and unity power factor during faulty conditions. The proposed DSTATCOM controller has undergone experimental validation in the laboratory.

Deep Adaptive Aec: Hybrid of Deep Learning and Adaptive Acoustic Echo Cancellation

Abstract: In this paper we integrate classic adaptive filtering algorithms with modern deep learning to propose a new approach called deep adaptive AEC. The main idea is to represent the linear adaptive algorithm as a differentiable layer within a deep neural network (DNN) framework. This enables the gradients to flow through the adaptive layer during back propagation and the inner layers of the DNN are trained to estimate the playback reference signal and the time-varying learning factors. The proposed approach combines the power of DNNs with adaptive filters. Experimental results show the effectiveness of the proposed method in scenarios where the echo path changes continuously and signal-to-echo ratio (SER) and signal-to-noise ratio (SNR) are low. Furthermore, compared to fully DNN-based baseline methods, integrating adaptive algorithm consistently improves performance and leads to easier training using smaller models.

Lawson-Norm-Based Adaptive Filter for Channel Estimation and In-Car Echo Cancellation

Abstract: We proposed a Lawson-norm-based adaptive estimation algorithm within the affine-project theory framework and give a name of Lawson-norm adaptive filter (LNAF) algorithm. The LNAF algorithm is derived, analyzed, and simulated for echo cancellation when background noise is impulsive, which is realized and implemented via using Lawson norm of past errors to take a sliding window on cost function to speed up convergence and achieve robustness for impulsive noises for colored signal. Simulations based on measured data are used for in-car echo cancellation and channel estimation to verify the LNAF algorithm’s performance with different inputs, which prove that the LNAF algorithm is superior to the popular AP algorithms under different impulsive interferences.

Robust Maximum Correntropy Criterion Subband Adaptive Filter Algorithm for Impulsive Noise and Noisy Input

Abstract: In this brief, an improved maximum correntropy criterion subband adaptive filter (MCC-SAF) algorithm is presented, which has excellent performance for alleviating the effect of impulsive noise and noisy input. Though the MCC-SAF algorithm performs well in the face of impulsive interference due to the advantage of the correntropy-based cost function, it yields estimated error when the system input is disturbed by noise. Profiting from the property of unbiased criterion, we propose a bias-compensated MCC-SAF algorithm (BC-MCC-SAF) by introducing a biased compensated term into the MCC-SAF algorithm. Besides, the computational complexity of the BC-MCC-SAF is investigated. Simulation results for system identification under various input signals have illustrated that the presented algorithm both retains robust achievement for the impulsive interference circumstance and achieves lower stable state error and brilliant tracking ability for noisy input.

Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction

Abstract: This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

Adaptive Hammerstein Filtering via Recursive Non-Convex Projection

Abstract: Hammerstein model with a static nonlinearity followed by a linear filter is commonly used in numerous applications. This paper focuses on adaptive filtering techniques for parameter identification of Hammerstein systems and output prediction of nonlinear systems. By formulating the underlying filtering problem as a recursive bilinear least-squares optimization with the non-convex feasible region constraint, we develop a recursive non-convex projected least-squares (RncPLS) algorithm based on alternating direction method of multipliers (ADMM). The RncPLS algorithm alternates between implementing ridge regression and projecting on the non-convex feasible set, which successively refines the system parameters. The convergence and accuracy properties of the proposed RncPLS algorithm are theoretically investigated. Moreover, extensive simulation results in the context of system identification, nonlinear predication, and acoustic echo cancellation, are also included to demonstrate the performance characteristics of the proposed algorithm.

A Secondary Path-Decoupled Active Noise Control Algorithm Based on Deep Learning

Abstract: Active noise control (ANC) systems are widely used to cancel unwanted noise. However, for high-level noise, the residual error signal cannot be fully eliminated because of the nonlinearity of the secondary path, resulting in the diverging of the adaptive filter. In this letter, we propose a secondary path-decoupled ANC (SPD-ANC) algorithm based on deep learning. Specifically, the secondary path decoupled module consisting of two time-domain convolutional recurrent networks, one for modeling the nonlinear secondary path and the other for modeling the reverse process, is employed to calculate the secondary path-decoupled (SPD) error signal. The control signal is then generated by an adaptive filter that is optimized towards minimizing the SPD error signal. Simulation results indicate that the proposed method outperforms the conventional ANC methods under different conditions.

LMS and NLMS Algorithms for the Identification of Impulse Responses with Intrinsic Symmetric or Antisymmetric Properties

Abstract: In applications involving system identification problems, some characteristics of the impulse response of the system to be identified are usually exploited to design adaptive algorithms with improved performance. In this context, this paper focuses on the identification of systems that own intrinsic symmetric or antisymmetric properties, which can be further formulated by using a combination of bilinear forms. Based on such an approach, the least-mean-square (LMS) and normalized LMS (NLMS) algorithms with symmetric/antisymmetric properties (termed here LMS-SAS and NLMS-SAS) are proposed. Simulation results are shown confirming the improved convergence speed achieved by the proposed algorithms as compared to the conventional LMS and NLMS counterparts for different operating scenarios.

M-estimate affine projection spline adaptive filtering algorithm: Analysis and implementation

Abstract: This paper investigates the M-estimate affine projection spline adaptive filtering (MAPSAF) algorithm, which utilizes a modified Huber function with robustness against impulsive interference, and employs historical regression data to update the weight and the knot vector estimates for nonlinear filtering tasks. The detailed convergence and steady-state analyses of MAPSAF are also carried out in the mean and mean-square senses. In addition, an improved MAPSAF by exploiting the combined step sizes, called the CSS-MAPSAF algorithm, is derived to speed up the convergence on the premise of low steady-state misalignment. Numerical experiments in nonlinear system identification and nonlinear acoustic echo cancellation problems corroborate the theoretical performance analysis and the superiority of the proposed algorithms.

Novel VLSI Architecture for Fractional-Order Correntropy Adaptive Filtering Algorithm

Abstract: Conventional adaptive filters, which assume Gaussian distribution for signal and noise, exhibit significant performance degradation when operating in non-Gaussian environments. Recently proposed fractional-order adaptive filters (FoAFs) address this concern by assuming that the signal and noise are symmetric $\alpha $ -stable random processes. However, the literature does not include any VLSI architectures for these algorithms. Toward that end, this article develops hardware-efficient architecture for fractional-order correntropy adaptive filter (FoCAF). We first reformulate the FoCAF for its efficient real-time VLSI implementation and then demonstrate that these reformulations cause negligible performance degradation under the 16-bit fixed-point implementation. Using this reformulated algorithm, we design an FoCAF architecture. Furthermore, we analyze the critical path of the design to select the appropriate level of pipelining based on the sampling rate of the application. According to the critical-path analysis, the FoCAF design is pipelined using retiming techniques to obtain delayed FoCAF (DFoCAF), which is then synthesized using $\mathbf {45}$ -nm CMOS technology. Synthesis results reveal that DFoCAF architecture requires a minimal increase in hardware over the prominent least mean square (LMS) filter architecture and achieves a significant increase in the performance in symmetric $\alpha $ -stable environments where LMS fails to converge.

Affine Projection Champernowne Algorithm for Robust Adaptive Filtering

Abstract: The recently proposed affine projection Versoria (APV) algorithm has been widely used over other affine based algorithms due to its robustness against impulsive noises. However, the performance of the APV algorithm suffers from high steady state misalignment. In order to overcome this, we propose affine projection Champernowne adaptive filter (APCMAF) in which instead of taking Versoria function as a cost function we have used the probability density function of the Champernowne distribution as a cost function and data reuse technique. The proposed APCMAF algorithm provides low steady-state misalignment in impulsive noise environment. To verify the performance of the APCMAF algorithm, a set of simulation study has been done in system identification scenarios which confirms that the APCMAF provides better steady state performance with improved convergence performance over other existing algorithms in impulsive noise environments. Further, the bound of learning rate for stable convergence has been also derived and a detailed comparison of computational complexity is also presented.

Full Mean-Square Analysis of Affine Combination of Two Complex-Valued LMS Filters for Second-Order Non-Circular Inputs

Abstract: The affine combination of two complex-valued least-mean-squares filters (aff-CLMS) addresses the trade-off between fast convergence rate and small steady-state misadjustment error. However, a rigorous analysis of the aff-CLMS algorithm for second-order non-circular inputs is still under investigation. To this end, the focus in this letter is on the full mean-square analysis of the aff-CLMS algorithm, in which the transient analyses of the mixing parameter, as well as the standard and complementary weight-error covariance matrices, are completed. In addition, we derive the closed-form solutions of the steady-state weight-error power and its complementary version of the aff-CLMS. Finally, the effectiveness of the theoretical analysis is supported by computer simulations.