NLMS Algorithm Research Articles

Data-Reuse Extended NLMS Algorithm Based on Optimized Time-Varying Step-Size for System Identification

Data-Reuse Extended NLMS Algorithm Based on Optimized Time-Varying Step-Size for System Identification

Bidirectional Multi-Spectral Vibration Control: Insights from Automotive Engine Mounting Systems in Two-Dimensional Structures with a Damaged Vertical Active Element

Active mounting systems have become more prevalent in recent years to effectively mitigate structure-induced vibration across the automobile chassis. This trend is particularly evident in engine mounts. Considerable research has been dedicated to this approach owing to its potential to enhance the quietness and travel comfort of automobiles. However, prior research has concentrated on a limited spectrum of specific vibrations and noise control or has been restricted to vertical vibration control. This article describes the modeling, analysis, and control of a source structure employing a multidirectional active mounting system designed to closely simulate the position and direction of an actual automobile engine mount. A piezoelectric stack actuator is connected in series to an elastic (rubber) mount to form an active mount. The calculation of the secondary force required for each active mount is achieved through the application of harmonic excitation forces. The control signal can also reduce vibrations caused by destructive interference with the input signal. Furthermore, horizontal oscillations can be mitigated by manipulating the parameters via dynamic interconnections of the source structure. We specifically examined the level of vibration reduction performance in the absence of a vertical active element operation and determined whether the control is feasible. Simulation outcomes demonstrate that this active mount, which operates in both the vertical and horizontal directions, effectively mitigates excitation vibrations. Furthermore, a simulation was conducted to mitigate the vibrations caused by complex signals (AM and FM signals) and noise. This was achieved by monitoring the system response using an adaptive filter NLMS algorithm. Adaptive filter simulations demonstrate that the control efficacy degrades in response to complex signals and noise, although the overall relaxation trend remains unchanged.

A bias-compensated NLMS algorithm based on arctangent framework for system identification

A bias-compensated NLMS algorithm based on arctangent framework for system identification

Analysis of the LMS and NLMS algorithms using the misalignment norm

This work describes the convergence of the misalignment square norm (MSN) of the NLMS and LMS algorithms. It is shown that the MSN decrease is almost proportional to the mean square error (MSE). This allows obtaining simple expressions for the steady-state MSE. Also, it allows limiting the amount of time that the MSE takes large values and a curve that limits the MSE of LMS at any given time, independent of the input and background noise signals’ properties. Finally, it is also shown that many complications in the analysis of the LMS and NLMS algorithms can come from variations in the input vector square norm. The proposed analysis becomes very simple for long filters or constant power signals.

New variable step-size fast NLMS algorithm for non-stationary systems

New variable step-size fast NLMS algorithm for non-stationary systems

Optimal design of NLMS algorithm with a variable scaler against impulsive interference

Optimal design of NLMS algorithm with a variable scaler against impulsive interference

A variable regularization parameter widely linear complex-valued NLMS algorithm: Performance analysis and wind prediction

To overcome the conflict that the adaptive regularized complex-valued NLMS algorithms cannot have optimal performance when the regularization parameter is large or small, a widely linear complex-valued NLMS algorithm with the variable regularization parameter (VRP-WL-CNLMS) is proposed in this paper. The proposed algorithm can adaptively change the regularized parameter by exploiting a time-varying parameter that is obtained via making the power of noise-free a posteriori error minimum. A proper estimated method is provided to compute the power of the measured noise when the noise is unknown, and the moving-average method is employed to update the regularized parameter for avoiding large fluctuations. Then we provide the analysis of the transient and steady-state (TAS) behaviors of the proposed algorithm. Simulation results with different input signals illustrate that the VRP-WL-CNLMS algorithm has better advantages than other algorithms, and verify the theoretical validity of TAS analysis of the proposed algorithm in the system identification (SI) environment. Finally, the experimental results of wind prediction show that the predicted value of the proposed algorithm has a smaller error value with the original signal than the WL-CNLMS algorithm and can predict the signal better that can support the superiority of the proposed algorithm as well.

A novel channel estimation model for broadband wireless communication system using hybrid heuristic-based invariable step-size zero-attracting NLMS algorithm

A novel channel estimation model for broadband wireless communication system using hybrid heuristic-based invariable step-size zero-attracting NLMS algorithm

Implementation of a filter for active noise cancellation in earmuffs for agricultural tractor operations

The implementation of agricultural mechanization is of paramount importance in modern agriculture, providing the replacement of manual and animal work with the aid of modern machines, contributing to an increase in productivity in crops of increasingly larger areas. However, the use of these technologies can bring some inconvenient factors from an ergonomic point of view. Machine operators, for example, are exposed to high noise levels from tractors. Its harmful effects on health are not limited to hearing alone, but can also cause emotional, cardiovascular and fatigue disorders. There are two methods for noise attenuation: The passive method, which consists only of a physical barrier between the listener's ear canal and the external environment, and the active method, which is based on electronic equipment capable of generating a signal that the phenomenon of destructive wave interference. Agricultural operators often use passive noise mufflers which have the disadvantage of attenuating all sounds indiscriminately, thus limiting the operator's auditory perception. In this context, this work proposes an implementation of active noise cancellation that cancels only noise, allowing other ambient sounds to be heard normally. The work developed is of an experimental nature, where a noise source in an agricultural operation was analyzed, which was used as an input signal for the construction of the filter and simulation. The filter was able to perform active noise cancellation using the LMS and NLMS algorithms, but the NLMS presented better results.

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

Selective Partial Update Adaptive Filtering Algorithms for Block-Sparse System Identification

The block-sparse normalized least mean square (BS-NLMS) algorithm which takes advantage of sparsity, successfully shows fast convergence in adaptive block-sparse system identification, adaptive control, and other industrial informatics applications. It is also attractive in acoustic processing where long impulse response, highly correlated and sparse echo path are encountered. However, the major drawback of BS-NLMS is largely computational complexity. This paper proposes a novel selective partial-update block-sparse normalized least mean square (SPU-BS-NLMS) algorithm. Compared with conventional BS-NLMS for block-sparse system identification, the proposed elective partial-update block-sparse NLMS algorithm takes partial-update blocks scheme which is determined by the smallest squared Euclidean-norm at each iteration instead of entire block coefficients to save computations. Computational complexity analysis is conducted to help researchers select appropriate parameters for practical realizations and applications. Computer simulations on acoustic echo cancellation are conducted to verify the results and the effectiveness of the proposed algorithm.

Partial Update Simplified Fast Transversal Filter Algorithms for Acoustic Echo Cancellation

Robust algorithms applied in Acoustic Echo Cancellation systems present an excessive calculation load that has to be minimized. In the present paper, we propose two different low complexity fast least squares algorithms, called Partial Update Simplified Fast Transversal Filter (PU-SMFTF) algorithm and Reduced Partial Update Simplified Fast Transversal Filter (RPU-SMFTF) algorithm. The first algorithm reduces the computational complexity in both filtering and prediction parts using the M-Max method for coefficients selection. Moreover, the second algorithm applies the partial update technique on the filtering part, joined to the P-size forward predictor, to get more complexity reduction. The obtained results show a computational complexity reduction from (7L+8) to (L+6M+8) and from (7L+8) to (L+M+4P+17) for the PU-SMFTF algorithm and RPU-SMFTF algorithm, respectively compared to the original Simplified Fast Transversal Filter (SMFTF). Furthermore, experiments picked out in the context of acoustic echo cancellation, have demonstrated that the proposed algorithms provide better convergence speed, good tracking capability and steady-state performances than the NLMS and SMFTF algorithms.

Foetus Heartbeat Extraction

Fetal Electrocardiogram Extraction from Mother's Womb is the subject of this report, which includes a thorough examination and implementation. The Fetal ECG is made up of various thoracic and abdominal ECG signals from a pregnant lady. For estimating corrupted signals due to additive noise or interference, the Adaptive Noise Cancellation Technique is used. These algorithms are used to update the weights of an adaptive filter in the Adaptive Noise Cancellation system: The algorithms Least Mean Square, Normalized Least Mean Square, and Leaky Least Mean Square are utilized. This method can be used in both single-input, single-output (SISO), and multiple-input, single-output (MISO) systems (MISO). When compared to the LMS and LLMS algorithms, the NLMS algorithm performs better in both systems.

Variable Step-Size $\ell _{0}$-Norm Constraint NLMS Algorithms Based on Novel Mean Square Deviation Analyses

This paper proposes variable step-size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> -norm constraint normalized least mean square ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> -NLMS) algorithms for sparse channel identification. The mean square deviation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> -NLMS algorithm is newly analyzed to obtain the optimal step size by reflecting the correlated property of the inputs. The mean square deviation estimations are derived in both the absence and presence of information on measurement noises. The variable step-size schemes are derived by minimizing the forthcoming mean square deviations. A reset algorithm is introduced to consider the sudden change in the unknown system in practical situations. Simulations demonstrate that the proposed variable step-size algorithms have better performances than the existing algorithms in sparse system identification and acoustic echo cancellation scenarios.

Analysis of jamming and anti jamming techniques for Galileo GNSS

The Global Positioning System (GPS) is the oldest and the most-used navigation system in the world due to its manifold and diverse applications. But it has several drawbacks in terms of accuracy and interference as well. At a later stage, Global Navigation Satellite System (GLONASS) was developed to address these problems but it could not live up to the expectations as well. The most recent as well as advanced development was made in the year 2016 in the form of Galileo GNSS. It is said to be the youngest satellite navigation system which possesses a higher tracking accuracy and speed than both of GPS and GLONASS. This paper makes an attempt to implement the chirp jamming in order to jam the acquire signal, followed by anti-jamming using NLMS algorithm of the Galileo GNSS to retrieve back the original signal, which is till now lacking in the existing literature for navigation systems.

Non-Convex Projected Nlms Algorithm for Adaptive Nonlinear Prediction

Non-Convex Projected Nlms Algorithm for Adaptive Nonlinear Prediction

Phase synchronization in the system of supply voltage parameters correction by the adaptive filtration method

An adaptive phase-locked loop is considered, which makes it possible to improve the stability of synchronous active filters, to achieve compensation for high-quality harmonic components of the current consumed by autonomous objects. Therefore, the use of such an active filter will improve the power quality and the normal trouble-free operation of the equipment in general. A control system with an adaptive phase-locked loop often has the advantages of a reliable and efficient control system. It makes it possible to quickly respond to dynamic changes in the load, which is typical for the operation of functional equipment of autonomous objects. The control system becomes more flexible, reliable, efficient and provides an instantaneous value of the compensation current from the measured values of the load current curve. Calculations can be carried out in real time. LMS, NLMS, RLS algorithms are considered as adjusting the weighted adaptive phase-locked loop in the system for correcting the supply voltage parameters. Based on the simulation of algorithms carried out in the MATLAB environment, a comparative analysis of their effectiveness is made. It is shown that the NLMS algorithm has the highest suppression quality and minimum transient time. The NLMS algorithm works in real time and is recommended for use in the supply voltage correction system.

Selective Partial Update of NLMS Adaptive Filter Algorithm

In view of the low convergence speed of the LMS algorithm and the high computational complexity of the NLMS algorithm in wireless communication system, a selective partial update NLMS algorithm is proposed. This algorithm only updates part of the filter coefficients instead of all filter coefficients during each adaptation. It effectively reduce the complexity of classic adaptive filtering algorithms. In this paper, a selective partial update of the NLMS algorithm is deduced, and the simulation verification is carried out to compare with the LMS algorithm and the NLMS algorithm, and the superiority of the algorithm is verified.

Application of adaptive line enhancer based on NLMS algorithm in shaft-rate electric field signal detection

There is lots of useful signal feature in ship’s shaft-rate electric field. Extracting the signal feature is vital in underwater target recognition. In order to extract the feature of the shaft-rate electric field, the shaft-rate electric field is processed by ALE based on NLMS. The improved NLMS algorithm is proposed to construct an adaptive line-spectrum enhancer, and then the measured data of shaft-rate electric field is processed by ALE. The results show that the algorithm can effectively separate the weak shaft frequency electric field signal from the broadband background noise at low SNR. Compared with the ordinary ALE, the algorithm has more significant effect in improving SNR, faster convergence speed and smaller steady-state error, which greatly improves the detection ability of ship shaft frequency electric field.

Research on digital predistortion technology based on improved NLMS algorithm

In modern communication systems, digital predistortion (DPD) improve the linearity of PA and reduce the influence of nonlinear behavior on signal transmission. When identifying the inverse model of PA with traditional variable step size algorithm, the rate of convergence is slower, easy to be effected by noise and convergence instability exists at the optimal solution, which affects the optimization degree of the whole predistortion system. In this article, a digital predistortion technique based on improved NLMS algorithm is proposed to improve the accuracy of inverse model identification, convergence rate and anti-noise performance of the predistorter by changing the iteration function and introducing the autocorrelation matrix of the error of adjacent moments. The instability of the system at the optimal solution is reduced by introducing a new weight coefficient to update the correlation term. The simulation results show that the anti-noise performance, convergence rate and stability of the predistortion system based on New variable step size NLMS algorithm(NVNLMS) are obviously better than NLMS, and the out-of-band suppression of predistortion is optimized by 10 dB compared with the original system EVM is improved by 0.0031, ACPR is improved 9.1 dB.