Affine Projection Algorithm Research Articles

Affine Projection Algorithms with Novel Schemes of Variable Projection Order

Affine Projection Algorithms with Novel Schemes of Variable Projection Order

Adaptive current decoupling control scheme based on online multi-parameter identification for high-speed permanent magnet synchronous motor in fuel cell

The precise control of centrifugal hydrogen compressor (CHC) driven by high-speed permanent magnet synchronous motor (PMSM) is the key to the stable and efficient operation of hydrogen fuel cell (HFC). In this paper, an adaptive current deviation decoupling control (CDDC) strategy based on variable step size affine projection algorithm (VSS-APAs) is proposed to solve the high-speed PMSM control problem caused by current cross-coupling and parameter perturbation under complex operating conditions. Firstly, the step size is dynamically adjusted by VSS-APA through a normalized gradient descent to respond to system fluctuations, achieving rapid parameter identification during dynamic changes and accurate tracking in steady states. Secondly, stator inductance, magnetic flux linkage, stator resistance, and disturbance torque are identified in two-time-scale based on the characteristics of parameter changes, overcoming the rank deficiency in motor mathematical equations. Finally, utilizing online-identified motor parameters, the gains of the CDDC are adjusted, and compensation is applied for back electromotive force (BEMF) and disturbance torque, thereby ensuring the control accuracy of the motor under parameter perturbations and system disturbances. Experimental results demonstrate that the proposed method enhances the responsiveness and accuracy of the control strategy through efficient parameters identification. Consequently, it guarantees high-efficiency and stable performance of the CHC across different working conditions, markedly advancing the overall functionality and dependability of hydrogen energy system.

Improved affine projection algorithms with selective projection order for channel identification

Improved affine projection algorithms with selective projection order for channel identification

Widely linear complex kernel risk-sensitive loss affine projection algorithm and its variant

Widely linear complex kernel risk-sensitive loss affine projection algorithm and its variant

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

Background:The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. Method:The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. Result:The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108–142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. Conclusion:The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

A variable diagonal-matrix-step-size APA robust to impulsive noises

This study introduces a variable diagonal-matrix-step-size affine projection algorithm (APA), which shows robustness against to impulsive noises. Unlike the normal scalar step-size method, the independent step size for each input vector is used as the entry of the diagonal matrix, and the optimal step size at each time step is ascertained by conducting mean-square-deviation (MSD) analysis. In addition, a recursive equation of weight update has been derived without approximating posteriori error as priori error. Using the Lagrange multiplier method, we formulate a scalar step size affine projection algorithm that shows resilience to impulsive noises. This algorithm emerges as a solution to a constrained optimization problem. To further enhance convergence performance, a diagonal-matrix-step size is introduced and its MSD is analyzed. The step size at each time step is optimized by interpreting the MSD mathematically, resulting in the variable diagonal-matrix-step size APA (VDMSS-APA). Various simulations exhibit low steady-state error and fast convergence rate of the proposed algorithm.

Affine projection algorithms based on sigmoid cost function

In this paper, two new algorithms are proposed using modified sigmoid activation function as the cost function and combining with affine projection (AP) method, which are sigmoid affine projection sign algorithm (SAPSA) and sigmoid affine projection algorithm (SAPA). Conventional AP-type algorithms converge fast but are susceptible to impulsive interference. The sigmoid function has an upper bound, thus reducing the effect of impulse interference. The proposed algorithms combine the advantages of fast convergence speed of the affine projection method and strong noise resistance of the sigmoid cost function. Meanwhile the L2-norm constraint is used to adjust the updating formula of the weight vector, which further improves noise resistance. Several simulation experiments show that SAPSA and SAPA are superior to other AP-type algorithms under impulse interference.

On the correlation between the noise and a priori error vectors for standard and augmented affine projection algorithms

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.

Augmented Affine Projection Algorithm Based on Error Nonlinearity and Its Simplified Version

Augmented Affine Projection Algorithm Based on Error Nonlinearity and Its Simplified Version

Sparse kernel affine projection algorithm based post-distortion scheme using minimum symbol error rate criterion in visible light communication systems

Due to the capability of supporting high-speed data communication with low energy cost, visible light communication has emerged as a promising supplement to radio frequency communications. However, the inherent nonlinearity caused by the light emitting diode could distort the signal, and thus posing a challenging problem for the system performance. In order to mitigate the nonlinear effect, reproducing kernel Hilbert spaces based schemes are recently investigated, where the minimum symbol error rate (MSER) criterion is proposed as a more suitable criterion compared to the commonly used minimum mean square error (MMSE), since MSER directly optimizes the symbol error rate. In this paper, we present a new post-distortion scheme by exploiting the MSER criterion based kernel affine projection algorithm to combat the signal distortion. In addition, to avoid the linearly increased computational complexity, the surprise criterion is introduced for sparsification. Simulations are carried out to validate the performance, and the numerical results show that the proposed method can obtain remarkable performance improvement over the available algorithms.

A diffusion filtered-x affine projection algorithm for distributed active noise control

The well-known Filtered-x Affine Projection (FxAP) algorithm is usually deemed a better choice than the conventional Filtered-x Least-Mean-Square (FxLMS) algorithm when faster convergence speed is desired in active noise control applications. However, the improvement of its convergence performance is obtained at the expense of increased computational complexity. It is usually unrealistic to regulate multichannel ANC systems using the centralized AP algorithm. Recently, distributed diffusion adaptation schemes over acoustic sensor and actuator networks have been introduced to multichannel ANC systems, such as the Diffusion FxLMS algorithms. Distributed processing based on diffusion cooperation strategy allocates the computation load to the network nodes in a scalable manner. This paper proposes a distributed Diffusion Filtered-x AP (DFxAP) algorithm for multichannel ANC systems. Simulation results and computational complexity analysis show that the DFxAP algorithm outperforms the Diffusion FxLMS algorithm in convergence performance and has lower computational complexity compared to the centralized AP algorithm.

A New Efficient Multi-Channel Fast NLMS (MC-FNLMS) Adaptive Algorithm for Audio Teleconferencing systems

In this paper, we propose a new multichannel fast normalized least mean square (MC-FNLMS) algorithm for audio teleconferencing systems. The main idea behind the proposed MC-FNLMS algorithm is based on the introduction of a first order prediction process on the input noisy signals before the adaptive filtering process of each channel. The proposed algorithm is considered as an alternative solution for the existing algorithm in multi-channel applications. A full stability analysis of the proposed algorithm is derived along this paper. Simulation results of a comparison between the proposed MC-FNLMS algorithm and the two classical Multichannel Normalized Least Mean Square (MC-NLMS) and the Multichannel Affine Projection (MC-APA) algorithms, in terms of convergence speed, noise reduction and computational complexity, show the superiority of the proposed algorithm on multichannel adaptive noise reduction.

A decision feedforward channel equalization system based pseudo affine projection (DFE-PAP) algorithm

This paper addresses the problem of channel equalization in the context of digital communication systems. The important problem in these systems is the inter-symbol interference (ISI) that distorts the signal quality. In this work, we propose a new adaptive equalizer based on a pseudo affine projection technique to equalize the channel. The proposed equalizer uses multi-pseudo affine projections of the channel output to update the filter equalizer. Our proposed solution uses the Decision Feedforward Equalization (DFE) structure to eliminate the ISI and accelerate the convergence speed by the multi-projection technique. Our proposed equalizer is noted DFE-PAP (Decision Feedforward Equalizer based Pseudo Affine Projection). We have done intensive experiments to evaluate the performance of the proposed DFE-PAP equalizer even in the case of dispersive channels. We have used objective and subjective criteria to compare the proposed DFE-PAP with the classical DFE Normalized Least Mean Square (DFE-NLMS) equalizer. All the obtained results show the good performance of the proposed equalizer.

Robust multitask diffusion normalized M-estimate subband adaptive filter algorithm over adaptive networks

In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in distributed parameter estimation and target tracking of multitask network. However, its performance degrades under colored input signals or impulsive noises. To overcome these two drawbacks, this paper introduces the subband adaptive filter (SAF) into a multitask network for the first time, and a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed, which solves the global network optimization problem based on the modified Huber (MH) function in a distributed manner, achieves robustness to impulsive noise, and significantly improves the convergence performance of the MD-LMS algorithm. Compared with existing robust multitask diffusion affine projection algorithms (MD-APAs), the computational complexity of the proposed MD-NMSAF algorithm is greatly reduced. In addition, we analyze the mean and mean-square stability conditions of MD-NMSAF, provide theoretical models characterizing the network mean square deviation (MSD) behaviors of transient and steady-state, and further verify their correctness by computer simulations. Simulation results under different network topologies, input signals, filter lengths and impulsive noise types fully demonstrate the performance advantages of the proposed MD-NMSAF algorithm over its competitors in terms of steady-state estimation accuracy and convergence speed.

Switched Combination of Simplified Kalman Filter and Affine Projection Algorithm for Acoustic Feedback Cancellation

Acoustic coupling between the microphone and the loudspeaker is a major issue in open-fit digital hearing aids. When compared to a close-fit hearing aid, an open-fit dramatically reduces signal quality and limits the potential maximum stable gain. Adaptive feedback cancellation (AFC) is a practical method for reducing the influence of acoustic coupling. However, because to the high correlation between the loudspeaker signal and the incoming signal, it might induce bias in calculating the feedback path if not carefully considered, especially when the incoming signal is spectrally coloured, as in speech and music. For decreasing this bias, the prediction error method (PEM) is well recognised. In this paper we proposed a switched PEM based hybrid combination of simpliedkalman filter and affine projection algorithms (H-SKF-APA), with soft-clipping that allows for further increase in convergence/tracking rates, resulting in a better ability to recover from an unstable or howling state. Simulation results showed that the proposed algorithm performed better.

Variable Step Size Methods of the Hybrid Affine Projection Adaptive Filtering Algorithm under Symmetrical Non-Gaussian Noise

The idea of variable step-size was introduced into the Hybrid Affine Projection Algorithm (H-APA) and we propose two variable step size algorithms based on H-APA, which are called the Variable Step-Size Hybrid Affine Projection Algorithm (VSS-H-APA) and the Modified Variable Step-Size Hybrid Affine Projection Algorithm (MVSS-H-APA). These are two variable-step algorithms aim to further improve the robust performance and convergence speed of H-APA under non-Gaussian noise. This allows for faster convergence while maintaining stability. The MVSS-H-APA goes further than VSS-H-APA to estimate the noise in order to achieve better convergence performance. The proposed algorithm performs better than the existing algorithms in system identification under symmetric non-Gaussian noise.

Robust Constrained Affine-Projection-Like Adaptive Filtering Algorithms Using the Modified Huber Function

In this brief, two robust constrained affine-projection-like M-estimate (CAPLM) adaptive filtering algorithms are proposed, which solve the problem that the traditional constrained affine projection (CAP) algorithm is not robust to impulsive interference, and realize unbiased output in some applications where the desired signal is unavailable or unnecessary. Specifically, a modified Huber function (MHF) based robust AP-like (APL) minimization problem with constraints is defined and can be transformed into two different unconstrained optimization problems by using Lipschitz continuity and two different methods, and then CAPLM-I and CAPLM-II algorithms are obtained respectively. Both CAPLM algorithms can avoid a certain amount of computational complexity caused by the inversion of the input signal matrix in classical AP algorithm, and realize the robustness to impulsive interference. In addition, the mean square stabilities of them are analyzed, and the corresponding stable step size ranges are also given. Simulation results show that the proposed CAPLM-I and CAPLM-II algorithms perform well in system identification and beamforming applications in impulsive noise environment, and provide lower steady-state error and faster convergence speed than other compared algorithms.

Robust hybrid affine projection filtering algorithm under [formula omitted]-stable environment

A novel hybrid affine projection algorithm (H-APA) is proposed. This algorithm is considered to be a derivation of the affine projection sign algorithm (APSA). The APSA can be greatly affected by impulsive noise. To solve this problem, the proposed algorithm is applied with the robust mixed-norm algorithm (RMNA). The simulated results show that the proposed algorithm is insensitive to impulsive noise with fast convergence rate under the colored input condition. In addition, a simplified H-APA (SH-APA) is derived based on the proposed H-APA to reduce the complexity of the algorithm. Numerical simulations show that compared with the existing AP-type algorithms, the proposed algorithms have faster convergence speed.

Speech enhancement for noisy signals using adaptive algorithms

Speech enhancement is a signal processing technique used to improve the quality and intelligibility of speech recordings that contain noise or interference. Its main goal is to eliminate unwanted background noise while preserving the clarity and naturalness of the speech signal. This paper provides a comprehensive analysis of three widely used adaptive filtering algorithms, Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Affine Projection Algorithm (APA). The limitations of LMS, such as slow convergence and sensitivity to input variations, are addressed in this study. By incorporating normalization, NLMS improves convergence speed and robustness to input power levels. The Affine Projection Algorithm (APA) is known for its exceptional performance in non-stationary environments, achieved through subspace projection to estimate optimal filter coefficients, resulting in faster convergence and improved tracking capabilities. In this paper, the algorithms are compared using Signal-to-Noise Ratio (SNR), Mean-Square Error (MSE), and Root-Mean-Square-Error (RMSE) values.

Sparsity-Promoting Affine Projection Algorithm With Periodically-Updated Gain Matrix and Its Performance Analysis

Sparse system identification is often encountered in applications such as network and acoustic echo cancellation. This work applies the sparsity promoting method to the affine projection algorithm (APA) to develop a sparsity-promoting APA (SAPA). To reduce its computational complexity, the gain matrix of SAPA is periodically updated, which leads to a periodically updated gain matrix based SAPA (PSAPA). In addition, the steady-state and tracking performance of PSAPA is analyzed using an improved weighted energy conservation method, which takes account of the correlation between the adaptive filter weight vector and the noise vector. The results of performance analysis are also suited to other proportionate APAs (PAPAs). Simulation results verify the advantages of the proposed algorithms and show that the theoretical expressions on the steady-state and tracking performance can predict the stochastic behaviors well.