Least Mean Square Research Articles

Stochastic‐gradient‐based control algorithms for power quality enhancement in solar photovoltaic interfaced three‐phase distribution system

AbstractHere, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ‐normalised LMS (‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519.

Robust variable step size least mean fourth with quotient form-based control scheme for DVR operation

ABSTRACT A robust variable step size least mean fourth with a quotient form control scheme (RVSSLMFQ) is implemented for addressing power quality concerns and delivering clean voltage to sensitive loads through a dynamic voltage restorer (DVR). This method estimates the fundamental components from distorted grid voltages, featuring an automated process with adaptive time-varying step sizes and utilizing a quotient structure to reduce mean squared error (MSE) effectively. The suggested control scheme overcomes the stability and performance issues of the least mean fourth (LMF) algorithm by improving convergence and adapting well to varying step sizes. Furthermore, RVSSLMFQ achieves remarkable results with reduced settling time and fewer peak overshoots compared to the least mean square (LMS) and LMF control algorithms. For tuning the load voltage and DC-link voltage proportional-integral (PI) controllers, the improved grey wolf optimization technique (I-GWO) is applied. The results are evaluated through MATLAB simulations.

Optimization of Interference Suppression Algorithm for Polarization Sensitive Conformal Array in Complex Electromagnetic Environment

With the development of modern electronic technology, the electromagnetic environment is becoming increasingly complex, which poses a severe challenge to the normal work of radar, communication and other systems. Polarization sensitive array can more effectively distinguish and suppress interference signals by using polarization information of electromagnetic waves, and improve the signal-to-noise ratio (SNR) and anti-interference ability of the system. Conformal array can be closely attached to the carrier surface, reducing space occupation and improving signal receiving efficiency and accuracy. However, at present, polarization-sensitive conformal arrays still face technical problems in interference suppression, especially in complex electromagnetic environment, how to optimize interference suppression algorithms to improve system performance has become the research focus. In this paper, an adaptive interference suppression algorithm combining spatial-polarization domain processing is proposed. The algorithm digitally processes the received signal, extracts spatial and polarization domain information, constructs the joint feature vector of the signal in spatial and polarization domain, and designs a joint filter in spatial and polarization domain. The minimum mean square error (MMSE) criterion and the least mean square (LMS) algorithm are used to update the weight vector of the filter in real time, enabling the filter to automatically adapt to changes in the electromagnetic environment and achieve optimal interference suppression. Simulation experiments show that the algorithm exhibits good stability and adaptability under different electromagnetic environments, effectively suppressing interference and extracting the desired signal. Compared with traditional interference suppression algorithms based solely on spatial domain, the joint spatial-polarization domain adaptive interference suppression algorithm significantly improves the interference suppression ratio (ISR) and signal-to-interference-plus-noise ratio (SINR) performance indicators, verifying that the introduction of polarization domain information can enhance the effect of interference suppression.

A Finite-Time Path Tracking Control Scheme for Unmanned Vehicles Based on Multidimensional Taylor Network and Adaptive Filtering

Aiming at the path tracking control problem of unmanned vehicles under the conditions of model uncertainty and measurement noise, a finite-time path tracking control scheme based on multidimensional Taylor network (MTN) is proposed. The MTN model is used to characterize the uncertainty of the unmanned vehicle model, and the improved back propagation (BP) algorithm is used as its learning algorithm; an adaptive MTN filter is designed to filter out the measurement noise, MTN is used as a filter, and the least mean square (LMS) algorithm is used as its learning algorithm; an MTN finite-time controller is designed to perform precise path tracking control on the unmanned vehicle, which can quickly and accurately track the reference path; according to the finite-time control theory, the convergence proof verifies the effectiveness of the proposed method through unmanned vehicle simulation experiments.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Lateral vibration reduction in vertically installed shafts by active control with LMS algorithm

Machines such as generators and electric motors that use vertical shafts must avoid noise and heat problems caused by vibration. Especially in the case of vertical hydro generators, vibrations caused by water flow through the turbine and imbalance of components can lead to poor performance, premature wear of components, and even system failure. Therefore, effective control of shaft vibration is very important for system maintenance and stable operation. To solve this problem, an active control method with Least Mean Squares(LMS) algorithm were applied to the lateral vibration. In the study, the vertical shaft and the active bearing were modeled using the Finite Element Method (FEM), and the external hydraulic force was employed with an unbalanced mass. Simulations were conducted and effectiveness of the simulation results was verified through the active control experiments. These results show that active bearings have the potential to solve vibration problems and extend the life of machines with vertical shafts, such as generators or electric motors.

A low-complexity error-feedback lattice-equalizer with phase tracking for underwater acoustic communications.

Recursive least squares (RLS)-based equalizers are hindered by their high complexity in underwater acoustic (UWA) communications. This article proposes an adaptive equalizer with a phase tracking method for the UWA communication, named the error-feedback lattice-equalizer (EFLE). First, we derive the algorithm for recursively solving the least squares problem from EFLE, introducing a lattice structure using time and order updates, thereby reducing the complexity to be linearly related to its length. The error-feedback mechanism used in computing reflection coefficients ensures the numerical stability of the algorithm. By focusing on the rapid tap rotation in time-varying channels, we design phase tracking in EFLE to further improve equalization performance. To verify the bit error rate (BER) performance of the proposed EFLE, we study the UWA communication system and conduct UWA simulations and at-sea experiments. Comparisons include linear complexity equalizers such as least mean square (LMS), leaky LMS, least mean mixed-norm, and ϵ-normalized LMS equalizers, and quadratic complexity RLS equalizers. At-sea experiment results show that the BER performance of EFLE significantly outperforms its counterparts.

Adaptive approach to spectrum monitoring in cognitive radio networks through signal detection optimization

The article considers the improvement of the adaptive algorithm of the spectral monitoring method for cognitive radio networks by introducing adaptive wavelet transforms and filters. The use of adaptive Morle and Dobechy wavelet transforms, as well as adaptive Kalman, LMS, and RLS filters is proposed, which allows dynamically changing parameters depending on the conditions of the radio environment. The comparative analysis with traditional methods showed that adaptive methods significantly increase the efficiency of signal detection in conditions of low SNR values, reducing the noise level, improving the accuracy of signal detection and reducing the probability of false alarms. The results of the study confirm the perspective of using adaptive methods to increase the reliability and efficiency of spectral monitoring in real operating conditions.

Sound source localization based on microphone array mounted on unmanned aerial vehicles

Abstract Microphone array installed on unmanned aerial vehicles (UAV) can be used for acoustic detection in scenes, such as detecting noise sources in factories or monitoring corona in power grids. However, using acoustic sensors on UAV has great challenges. The motor rotation of UAV and the non-stationary noise generated by propeller make it difficult for microphone array to obtain useful information for sound source localization. In order to reduce self-noise, an improved least mean square (LMS) adaptive filtering algorithm is proposed. Combining filtering algorithm and multiple signal classification (MUSIC) algorithm to locate the sound source, the noise spectrum characteristics of UAV in outdoor free flight mode are analyzed. The microphone array is used to collect data and realize sound source localization in low Signal-to-Noise Ratio (SNR) environment. The simulation and experimental results show that the algorithm improves the robustness of the sound source localization algorithm in low SNR environment, and realizes the real-time localization of the target sound source during the flight of UAV with microphone array.

Application of ARIS and Adaptive Filtering Algorithm in Underwater Acoustic Communication

As the core of marine communication, underwater acoustic communication technology significantly improves the performance of underwater communication by using technologies such as modulation, multiple access and anti-interference. Among them, adaptive filtering technology plays a key role in eliminating noise and channel equalization. This paper explores the novel application of acoustic reconfigurable intelligence surface (ARIS) and adaptive filtering in underwater acoustic communication. It introduces the concept of ARIS-assisted underwater acoustic communication, integrating the least mean squares (LMS) algorithm to dynamically estimate and mitigate oceanic noise. This approach further enhances signal clarity and communication quality by leveraging orthogonal frequency division multiplexing (OFDM) technology. Simulation results show that the underwater acoustic channel with ARIS is superior to the traditional channel without ARIS in terms of bit error rate and anti-interference performance. This technology significantly enhances the reception quality of underwater communication signals, making it suitable for applications in marine monitoring, military communication, and other complex environments.

A linearly constrained framework for the analysis of the deficient length least-mean square algorithm

Most adaptive system identification analyses assume the length of the adaptive filter to match the length of the unknown system response. This assumption tends to be unrealistic, and its conclusions may not apply to some practical applications. The behavior of deficient length adaptive filters has been studied using different approaches. This work formulates the deficient length adaptive system problem using a Linearly Constrained Minimum Mean Squared Error (LCMMSE) framework. This new formulation leads to a very interesting interpretation that allows the utilization of results from the study of constrained adaptive filters to understand the behavior of the deficient length LMS adaptive filter. The reduced number of coefficients is formulated as a linear optimization constraint that defines a projection onto the feasible space for the adaptive weight vector. We derive analytical models for the mean and mean-square behavior of the adaptive weights. In addition, we derive an analytical model for the variance of the steady-state squared estimation error which provides important design information. Simulation results show excellent matching between theory and actual algorithm behavior.

A novel method for detecting cardiac arrhythmias in electrocardiogram signals using the ANFIS classifier

In general, ventricular repolarization instability with Premature Activations (PA) plays a significant role in the detection of arrhythmia using an electrocardiogram (ECG) signal. However, clinical detection of this instability is challenging. In this work, a unique technique for analyzing QT Interval (QTI) dynamics unreliability and other abnormalities in the ECG signal are utilized to detect ventricular tachycardia based on the identified anomalies. Further, a feature extraction method based on Chopped Displacement Gaussian (CDGa) is adopted and the features are retrieved from the CDGa signal and its size is reduced using Fisher Discrimination Analysis (FDA). Also, the results of the FDA are fed into the Adaptive Neuro-Fuzzy interference (ANFIS) classifier. The performance of the ANFIS classifier is enhanced in this study by integrating the Grasshopper optimization algorithm for optimizing network parameters. As a result, the values in the signals are predicted using an adaptive Least Mean Square (LMS)-based prediction algorithm, which is utilized to forecast the future status of VT-affected patients. Results indicate that the proposed work has an overall accuracy of 96 % which is higher than any other conventional methods. Also, the various additional performance criteria are tested, and it is demonstrated that the CDGa with adaptive LMS outperforms all previous approaches.

An ADPLL-Based GFSK Modulator with Two-Point Modulation for IoT Applications.

To establish ubiquitous and energy-efficient wireless sensor networks (WSNs), short-range Internet of Things (IoT) devices require Bluetooth low energy (BLE) technology, which functions at 2.4 GHz. This study presents a novel approach as follows: a fully integrated all-digital phase-locked loop (ADPLL)-based Gaussian frequency shift keying (GFSK) modulator incorporating two-point modulation (TPM). The modulator aims to enhance the efficiency of BLE communication in these networks. The design includes a time-to-digital converter (TDC) with the following three key features to improve linearity and time resolution: fast settling time, low dropout regulators (LDOs) that adapt to process, voltage, and temperature (PVT) variations, and interpolation assisted by an analog-to-digital converter (ADC). It features a digital controlled oscillator (DCO) with two key enhancements as follows: ΔΣ modulator dithering and hierarchical capacitive banks, which expand the frequency tuning range and improve linearity, and an integrated, fast-converging least-mean-square (LMS) algorithm for DCO gain calibration, which ensures compliance with BLE 5.0 stable modulation index (SMI) requirements. Implemented in a 28 nm CMOS process, occupying an active area of 0.33 mm2, the modulator demonstrates a wide frequency tuning range of from 2.21 to 2.58 GHz, in-band phase noise of -102.1 dBc/Hz, and FSK error of 1.42% while consuming 1.6 mW.

Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

A 0.00179 mm2/Ch Chopper-Stabilized TDMA Neural Recording System With Dynamic EOV Cancellation and Predictive Mixed-Signal Impedance Boosting.

This article presents a digitally-assisted multi-channel neural recording system. The system uses a 16-channel chopper-stabilized Time Division Multiple Access (TDMA) scheme to record multiplexed neural signals into a single shared analog front end (AFE). The choppers reduce the total integrated noise across the modulated spectrum by 2.4 × and 4.3 × in Local Field Potential (LFP) and Action Potential (AP) bands, respectively. In addition, a novel impedance booster based on Sign-Sign least mean squares (LMS) adaptive filter (AF) predicts the input signal and pre-charges the AC-coupling capacitors. The impedance booster module increases the AFE input impedance by a factor of 39 × with a 7.1% increase in area. The proposed system obviates the need for on-chip digital demodulation, filtering, and remodulation normally required to extract Electrode Offset Voltages (EOV) from multiplexed neural signals, thereby achieving 3.6 × and 2.8 × savings in both area and power, respectively, in the EOV filter module. The Sign-Sign LMS AF is reused to determine the system loop gain, which relaxes the feedback DAC accuracy requirements and saves 10.1 × in power compared to conventional oversampled DAC truncation-error ΔΣ-modulator. The proposed SoC is designed and fabricated in 65 nm CMOS, and each channel occupies 0.00179 mm2 of active area. Each channel consumes 5.11 μW of power while achieving 2.19 μVrms and 2.4 μVrms of input referred noise (IRN) over AP and LFP bands. The resulting AP band noise efficiency factor (NEF) is 1.8. The proposed system is verified with acute in-vivo recordings in a Sprague-Dawley rat using parylene C based thin-film platinum nanorod microelectrodes.

Dispersed-sparsity-aware LMS algorithm for scattering-sparse system identification

We develop a least-mean-squares (LMS) algorithm to identify scattering-sparse systems, where the few significantly large coefficients of the unknown impulse response are dispersed along the full length, instead of grouped into clusters. Many wireless communication channels such as the underwater acoustic channels, cellular communication channels and aviation channels are typical scattering-sparse systems. In the proposed strategy, the difference between the ℓ1-norm and ℓ∞,1-norm of the uniformly-divided tap-weight vector of the adaptive filter is utilized as a penalty and is introduced into the mean-square-error cost function, where the ℓ∞,1-norm of the tap-weight vector is used to locate the unknown large coefficients in view of the characteristics of dispersed sparsity. A dispersed-sparsity-aware LMS (DS-LMS) algorithm is then proposed by following the stochastic subgradient method. We study the mean and mean-square behaviors of the proposed algorithm. We also provide theoretical guidelines for the parameter settings that ensure that the proposed DS-LMS algorithm converges to a lower mean-square-deviation (MSD) level than the traditional LMS algorithm. Simulation results verify the effectiveness of the proposed DS-LMS algorithm, and correctness of the theoretical findings.

A Wideband Timing Mismatch Calibration Design for Time-Interleaved Analog-to-Digital Converters with Fast Convergence

This paper presents a design for timing mismatch calibration in a TIADC (Time-Interleaved Analog-to-Digital Converter) with wideband inputs. By exploiting the approximately linear relationship between the autocorrelation properties of sub-ADCs and timing mismatch, we achieve rapid convergence of error estimation. A low-cost detection method is proposed based on the convergent monotonicity of the Least Mean Square (LMS) algorithm, which can automatically correct the calibration direction when the input signal goes beyond the Nyquist zone. Physical test results indicate that the spurs caused by timing mismatch can be suppressed by 26–30 dB using the proposed method.

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Abstract To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

Multiplexing and demultiplexing acoustic orbital angular momentum beams in complex environments

The spiral sound waves carrying orbital angular momentum (OAM) are significantly more efficient in delivering information and energy than the ordinary plane waves. A major bottleneck in exploring the use of acoustic OAM is the excitation and detection of OAM in an arbitrary environment. Here we propose a scheme combining the multichannel least-mean-square (LMS) algorithm and an improved virtual rotating receiver (VRR) method to multiplex and demultiplex acoustic OAM beams in various complex environments. We experimentally demonstrate that the LMS-VRR scheme can excite and detect the OAM beams in indoor and outdoor real-world environments. We use the LMS algorithm to multiplex OAM beams with up to 40 channels at a circular microphone array by compensating the source signals with the equalization filters. On the other hand, the circular microphone array works as a VRR to demultiplex the OAM beams from the rotational Doppler effect. Additional preprocesses are taken to eliminate the plane wave pollution for the VRR method. The acoustic OAM beams have been multiplexed and demultiplexed over 20 m away from the sound sources on a shady lane. Our work has shown practical prospects in covert communication, and shall further unleash the application potential of acoustic OAM beams by removing the restrictions on exciting and detecting OAM in complex environments.

Least mean square based adaptive control of active power filter

The proposed control scheme is based on the least mean square (LMS) algorithm. The LMS algorithm is employed to estimate the necessary reference tracking current for the active power filter (APF). The proposed control scheme aims to enhance the dynamic response of the APF and minimize steady-state error. The weights of the LMS technique are calculated based on the estimated current of the APF. This algorithm is employed to minimize the error difference between the desired system output and its actual output, known as the mean square error (MSE). The estimated weights are utilized to modify the reference current weights, enabling them to follow the intended current of the APF. The online adaption of the LMS method involves the real-time adjustment of the weights. The performance of the LMS-based APF control is evaluated through a simulation study in MATLAB/Simulink, where it is compared with the conventional control method.