Block LMS Research Articles

The RFI Fast Mitigation Algorithm Based on Block LMS Filter

The radio telescope possesses high sensitivity and strong signal collection capabilities. While receiving celestial radiation signals, it also captures Radio Frequency Interferences (RFIs) introduced by human activities. RFI, as signals originating from sources other than the astronomical targets, significantly impacts the quality of astronomical data. This paper presents an RFI fast mitigation algorithm based on block Least Mean Square (LMS) algorithm. It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block. This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem. The algorithm is tested using baseband data from the Parkes 64 m radio telescope’s pulsar observations and simulated data. The results confirm the algorithm’s effectiveness, as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

Power Optimized VLSI Architecture of Distributed Arithmetic Based Block LMS Adaptive Filter

In this paper, we are presenting a power-efficient Distributed Arithmetic (DA) based Block Least Mean Square (BLMS) Adaptive Digital Filter (ADF). The proposed DA BLMS architecture proposes a shared area-efficient Multiplier Accumulate Block that calculates both the partial filter products and the weight increment terms in the same module. It also uses Multiplexers (MUX) and Demultiplexers (DEMUX) which passes only L out of N inputs, where N and L are the filter length and chosen block size respectively, into the MAC thus helping in achieving the DA functionality along with reduced power consumption. Also, efficient truncation of the obtained error and weight update terms is performed by being able to select the non-zero-bit part of the signal to be fed back. The entire architecture is driven by a single slow clock which reduces the power consumption of the device further. On comparing with the best existing DA BLMS Structures, the proposed architecture uses 15% lesser power, 14% lesser EPS according to ASIC Synthesis, and for a filter length of N=16 and a block size of L=4 respectively.

Frequency Domain Adaptive Learning Algorithm for Thoracic Electrical Bioimpedance Enhancement

The Thoracic Electrical Bioimpedance (TEB) helps to determine the stroke volume during cardiac arrest. While measuring cardiac signal it is contaminated with artifacts. The commonly encountered artifacts are Baseline wander (BW) and Muscle artifact (MA), these are physiological and non-stationary. As the nature of these artifacts is random, adaptive filtering is needed than conventional fixed coefficient filtering techniques. To address this, a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario. The proposed block least mean square (BLMS) algorithm is mathematically normalized with reference to data and error. This normalization leads, block normalized LMS (BNLMS) and block error normalized LMS (BENLMS) algorithms. Various adaptive artifact cancellers are developed in both time and frequency domains and applied on real TEB quantities contaminated with physiological signals. The ability of these techniques is measured by calculating signal to noise ratio improvement (SNRI), Excess Mean Square Error (EMSE), and Misadjustment (Mad). Among the considered algorithms, the frequency domain version of BENLMS algorithm removes the physiological artifacts effectively then the other counter parts. Hence, this adaptive artifact canceller is suitable for real time applications like wearable, remove health care monitoring units.

WITHDRAWN: Low power implementation of adaptive block FIR filter design using offset binary coding

Abstract This paper presents a power efficient realization of block least mean square (BLMS) algorithm based adaptive finite impulse response filter (ADF) using offset binary coding (OBC). OBC method is one of the area optimization methods of distributed arithmetic (DA). In the DA scheme partial products of the filter are stored in a look-up table (LUT) followed by a shift accumulate unit (SA). The size of the LUT increases exponentially with length of the filter. Using the OBC method, the size of the LUT is reduced. Memory sharing concept is used to design the registers, which are in turn used for input sample generation. A high range of parallelism is included in the architecture that enhances its performance. The adaption delay is decreased by doing parallel LUT update, filtering and weight updation operations. The results prove that the proposed design consumes 42% less power in comparison to the best existing architectural designs.

Adaptive filtering techniques for velocity estimation of tomographic electric capacitance signals of two-phase gas/oil flows

The development of adaptive real-time flow velocity estimation algorithms for two-phase flows can contribute to monitoring the pipelines of various complex processes, such as energy, chemical, petroleum and nuclear industries. Among the different non-invasive tomography techniques, electrical capacitance tomography (ECT) is gaining increasing attention for its potential use in real-time imaging and characterization of multiphase flow systems. The nature of ECT signals for two-phase flows can significantly degrade the velocity estimation process with cross-correlation approaches. We address the unique challenges of such signals and propose a preprocessing technique to improve the performance and robustness of the velocity estimation algorithm. Two adaptive filters are used to estimate the velocity of a two-phase type flow. A least mean square (LMS) and a fast block LMS (FBLMS) are used to model the time delay between the two signals captured by the twin sensor (ECT). Performance of the proposed technique is assessed by applying it to ECT data obtained from an experimental flow rig. The computed estimates are then compared with the calculated velocity from tracking motion of bubbles captured by a high speed camera monitoring the two phase flow in the pipe. Results show that the proposed technique provides consistent results across various flow patterns, and is advantageous compared to cross-correlation based techniques, specially for chaotic flow conditions. Furthermore, the proposed estimation algorithms can be applied to other electric based tomographic techniques.

High-Performance Hardware Design of Block LMS Adaptive Noise Canceller for In-Ear Headphones

This article presents a high-performance hardware design of block least mean square adaptive noise canceller (ANC) for in-ear headphones applications. It is based on distributed arithmetic, which stores filter partial products in look-up table (LUT). The proposed technique splits LUTs into two smaller LUTs and stores the filter partial products in offset-binary-coding form. Furthermore, the splitted LUTs are shared to compute the filter output and coefficient-increment terms. A novel strategy is also presented to update LUT contents. Compared with the best existing design, the proposed ANC for 32nd filter order requires 50% less LUT words, 5.31% less adders, 28.57% high-throughput, and relatively less critical path-delay. From the implementation results, it is found that the logic utilization of the proposed design is significantly reduced while achieving better noise cancelling performance. For example, the proposed ANC for 32nd filter order provides noise reduction of about 6–20 dB and utilizes 1.80 times less LUTs and 1.71 times less flip-flops over the best existing design.

Analysis of the Unconstrained Frequency-Domain Block LMS for Second-Order Noncircular Inputs

The unconstrained frequency-domain block least mean square (UFBLMS) algorithm is a de facto standard in frequency-domain adaptive filtering, owing to its fast convergence for correlated input signals. However, although many complex-valued signals in real-world applications are second-order noncircular (improper), for computational convenience, existing mean square analyses of UFBLMS assume second-order circular (proper) input data. This, in turn, makes conventional mean square evaluations of UFBLMS inadequate. To address this issue, we propose a novel performance evaluation framework of UFBLMS, which makes it possible to investigate how the full second-order statistics of second-order noncircular correlated input signals, i.e., both their covariance and complementary covariance, influence the output error and the weight error of UFBLMS. A unified stability bound on the step-size of UFBLMS is next provided, enabling intuitive closed-form relations, in the steady-state stage, between the terms within full second-order error statistics, namely, the mean square error (MSE) and complementary MSE, and the input noncircularity. Furthermore, this proposed full second-order statistical framework is shown to enable more physical insight into the underlying physics of the contribution of the real and imaginary channels of UFBLMS, which cannot be achieved using the standard MSE analysis due to its insufficient degrees of freedom. Simulations and real-world experiments in the system identification setting support the analysis.

Mean-square performance of the modified frequency-domain block LMS algorithm

The mean weight vector of the normalized frequency-domain block least-mean-square (NFBLMS) algorithm cannot converge to the optimal solution in the mean-square error sense for the non-causal and under-modeling cases. A modified frequency-domain block least-mean-square (MFBLMS) algorithm was proposed to resolve this problem, which was claimed to have optimal steady-state performance. In this paper, we present a comprehensive statistical analysis of the MFBLMS algorithm in both the full- and under-modeling conditions. We first present the equivalent time-domain expressions for the update equation and the error vector of the MFBLMS, which allows us to carry out the performance analysis completely in the time domain. The analytical model for both the mean and mean-square performance of the MFBLMS are provided without assuming a specific input distribution, and the closed-form solution of the step-size bound is given. It is found that the upper step-size bound of the MFBLMS algorithm is much smaller than that of the NFBLMS algorithm for the correlated inputs, and the MFBLMS algorithm does not always achieve a better steady-state performance than the NFBLMS algorithm. Simulation results agree with our theoretical analysis quite well.

Range-Doppler Fast Block LMS algorithm for a DVB-T-based Passive Bistatic Radar

In this paper, we propose a novel algorithm based on fast block LMS (least mean square) adaptive filter, called RD-FBLMS (range-Doppler fast block LMS) for direct path and multipath interferences cancellation in DVB-T (digital video broadcasting—terrestrial) passive bistatic radars (PBRs). These interferences represent some of the major problems in PBR systems, generated by signals received from the illuminator of opportunity and their multiple reflections through the surveillance channel, which is only dedicated to receive targets echoes. It is known that classical adaptive filtering can only be used for cancellation of zero Doppler interferences. Based on successive reference signal modulations and suppressions of nonzero Doppler multipath interferences, the RD-FBLMS algorithm exhibits a faster convergence rate, a shorter processing time and a better cross-ambiguity function than well-known existing algorithms.

Fast Block LMS and RLS-Based Parameter Estimation and Two-Dimensional Imaging in Monostatic MIMO RADAR Systems With Multiple Mobile Targets

This paper presents novel adaptive schemes and the pertinent analysis for estimation of the number of targets, their associated radar cross section (RCS) values and Doppler velocities in monostatic MIMO radar systems. These schemes are based on the fast block least mean squares (FBLMS) and fast block recursive least squares (FBRLS) algorithms considering both stationary as well as mobile targets and/or radar platform. For the stationary scenario, schemes are proposed for estimation of RCS coefficients, target number and their angle, range locations. In addition, a procedure is also developed to obtain the 2D-image of the MIMO radar scene in angle-range dimensions. Analysis is presented for the first and second order moments of the observation as well as estimation errors resulting from the proposed FBLMS, FBRLS techniques, along with their global convergence. Both the proposed schemes are shown to converge globally to the optimal Wiener filter, which is lacking in existing schemes. Further, a Bayesian information criterion based selection rule is employed to enhance the estimation and imaging performance of the proposed adaptive frameworks. Cramer-Rao bounds are derived to characterize the mean squared error of the estimated RCS coefficients for stationary scenarios and also for joint RCS, Doppler velocity estimation in mobile MIMO radar scenarios. The FBLMS and FBRLS schemes are shown to have significantly lower computational complexities in comparison to existing MIMO radar schemes. Simulation results demonstrate the improved performance of the proposed schemes and also validate the derived analytical expressions.

Hardware Design for VLSI Implementation of Acoustic Feedback Canceller in Hearing Aids

Acoustic feedback is one of the major issues associated with the hearing aid users which limits the maximum amount of gain available for amplification and degrades the sound quality. In this paper, partitioned time-domain block LMS (PTBLMS) algorithm is proposed for efficient hardware realization of acoustic feedback cancellers (AFCs) in hearing aids. A full-parallel and a folded structure is derived using the proposed PTBLMS algorithm. The folded structure utilizing the time multiplexing of convolution and correlation operation and performing them in one arithmetic unit enables better hardware utilization. A low-complexity design is employed for realization of power normalization unit (for calculating normalized convergence factor) which involves squaring and division operations. The theoretical analysis illustrates that the proposed AFC structures offer L times higher throughput rate and requires proportionately less hardware resource than the existing one where L is the block length. ASIC synthesis results reveal that the proposed folded structure involves nearly 79$$\%$$% less area-delay product and 86$$\%$$% less energy per sample compared to the existing structure.

Convergence analysis of the modified frequency-domain block LMS algorithm with guaranteed optimal steady state performance

Although the bin-normalized frequency domain block LMS (NFBLMS) algorithm has theoretically very fast convergence speed, it suffers from convergence to a biased steady state solution when causality is not met or when the adaptive filter is of deficient length. A modified FBLMS (MFBLMS) algorithm with guaranteed optimal steady state performance has been proposed, but the theoretical analysis on its convergence properties has not been presented. This paper analyzes the convergence behavior of the algorithm by using the theory of asymptotically equivalent matrices. The eigenvalues of the matrix controlling the convergence behavior is proven to have the tendency to be equally distributed, and a theoretical eigenvalue spread is derived based on the first-order autoregressive (AR) signal model, which is significantly lower than that of the time domain LMS (TDLMS) algorithm. Therefore the convergence speed of the MFBLMS is significantly higher than that of the TDLMS algorithm for colored reference signal. Simulations are carried out to validate the convergence behavior predicted from the theoretical analysis.

FIR system identification for correlated noise using the convolution matrix, an investigation

A novel approach to finite-impulse response system identification is given. The method is formulated differently from ordinary least mean squares (LMS) or block LMS, which are traditional approaches and has a significant advantage of speed improvement when the correlation matrix has a large condition number. Unlike other approaches which are numerically complex, this method has a similar computational burden as LMS and gives the same optimal solution after convergence. It avoids transformation matrices by writing the system description in terms of a convolution matrix which has a special lower-triangular format. In this way the correlation matrix is different from conventional least-squares approaches and maintains a modest condition number as the correlation matrix in ordinary least-squares climbs high. The method has as wide a range of applications as ordinary LMS-based solutions but can also work on deterministic inputs.

A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising

Abstract Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of the heart. These signals, however, are often obscured by artifacts/noises from various sources and minimization of these artifacts is of paramount importance for detecting anomalies. This paper presents a thorough analysis of the performance of two hybrid signal processing schemes ((i) Ensemble Empirical Mode Decomposition (EEMD) based method in conjunction with the Block Least Mean Square (BLMS) adaptive algorithm (EEMD-BLMS), and (ii) Discrete Wavelet Transform (DWT) combined with the Neural Network (NN), named the Wavelet NN (WNN)) for denoising the ECG signals. These methods are compared to the conventional EMD (C-EMD), C-EEMD, EEMD-LMS as well as the DWT thresholding (DWT-Th) based methods through extensive simulation studies on real as well as noise corrupted ECG signals. Results clearly show the superiority of the proposed methods.

A comparative study of adaptive filters in detecting a naturally degraded bearing within a gearbox

The diagnosis of bearing faults at the earliest stage is critical in avoiding future catastrophic failures. Many diagnostic techniques have been developed and applied in for such purposes, however, these traditional diagnostic techniques are not always successful when the bearing fault occurs within a gearbox where the vibration response is complex; under such circumstances it may be necessary to separate the bearing vibration signature.This paper presents a comparative study of four different techniques for bearing signature separation within a gearbox. The effectiveness of these individual techniques were compared in diagnosing a bearing defect within a gearbox employed for endurance tests of an aircraft control system. The techniques investigated include the least mean square (LMS), self-adaptive noise cancellation (SANC) and the fast block LMS (FBLMS). All three techniques were applied to measured vibration signals taken throughout the endurance test. In conclusion it is shown that the LMS technique detected the bearing fault earliest.

Efficient very large‐scale integration architecture for variable length block least mean square adaptive filter

The authors made an analysis on computational complexity of block least mean square (BLMS) finite impulse response (FIR) filter and decompose the filter computation into M sub-filters, where M = N/L, N is the filter length and L is the block-size. The proposed decomposition scheme favours time-multiplexing the filtering computation and weight-increment term computation of BLMS algorithm. Using the proposed scheme, they have derived an efficient architecture for BLMS FIR filter. The proposed structure can be reconfigured for different filter lengths with negligible overhead complexity and it supports variable convergence factor. Besides, the proposed structure has 100% hardware utilisation efficiency and its register complexity is independent of block-size. Compared with recently proposed LMS-based FIR structure the proposed structure involves L times more multipliers, proportionately less adders and the same number of registers, and it offers L times higher throughput. Application specific integrated circuit (ASIC) synthesis results show that the proposed structure for block-size 4 and filter-length 64 involve 21.4% less area-delay product (ADP) and 26.6% less energy per sample (EPS) than those of the existing structure and offers 3.8 times higher throughput.

ECG SIGNALS NOISE REMOVAL: SELECTION AND OPTIMIZATION OF THE BEST ADAPTIVE FILTERING ALGORITHM BASED ON VARIOUS ALGORITHMS COMPARISON

The electrocardiogram (ECG) is generally used for the diagnosis of cardiovascular diseases. In many of the biomedical applications, it is necessary to remove the noise from ECG recordings. Several adaptive filter structures have been proposed for noise cancellation. Compared to the least mean square (LMS) method, the unbiased and normalized adaptive noise reduction (UNANR) algorithm has better performance, as mentioned in previous investigations. In this paper, we review various kinds of ECG noise reduction algorithms. To provide a detailed and fair comparison, all normalized LMS (NLMS), Block LMS (BLMS), recursive least squares (RLS) and UNANR algorithms are implemented and their performance have been assessed using the same dataset and compared to different state-of-the-art approaches. Then, the performance analysis of all five algorithms is presented and compared in term of mean squared error (MSE), computational complexity and stability. The obtained results revealed that RLS method is much more effective and powerful than other methods in ECG noise cancellation, and even better than UNANR. Then, in order to reach the best performance of the mentioned filter and also, to minimize the output signal error, the optimized parameters of the algorithm were defined and results were investigated. The obtained outcomes show that the best Lambda (λ) occurs between 0.05 and 0.9, so that the convergence rate of the optimized RLS filter is faster than others. It not only decreases the noise, but also the ECG waveform is better conserved. Furthermore, the introduced optimized method with adaptive threshold value would have great potential in biomedical application of signal processing and other fields.

Adaptive Joint Semi-blind Estimation of CFO and Channel for OFDM Systems

The large number of subcarriers, used in OFDM systems, increases the sensivity to frequency offset which results from a Doppler shift, due to mobile movement, as well as from a mismatch between the carrier frequencies at the transmitter and receiver. The problem of channel estimation in a practical OFDM receiver that suffers from CFO has been addressed in this paper. We first derived the modified MUSIC based blind CFO estimator. Next, we propose new joint training-based and joint semi-blind CFO and channel estimation for OFDM systems. Whereas the training-based estimator only assumes knowledge of pilot tones, the semi-blind estimator assumes knowledge of the virtual subcarriers (VSCs). For both estimators we derive a maximum likelihood (ML) algorithm for block processing and LMS algorithm for adaptive processing. It is demonstrated that the CFO estimators are able to perform well with a very small number of OFDM blocks. The performance of our estimators was investigated by simulations.

A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox

Diagnosing bearing faults at the earliest stages is critical in avoiding future catastrophic failures. Many techniques have been developed and applied in diagnosing bearings faults; however, these traditional diagnostic techniques are not always successful when the bearing fault occurs in gearboxes where the vibration response is complex; under such circumstances, it may be necessary to separate the bearing signal from the complex signal. In this paper, an adaptive filter has been applied for the purpose of bearing signal separation. Four algorithms were compared to assess their effectiveness in diagnosing a bearing defect in a gearbox, least mean square (LMS), linear prediction, spectral kurtosis and fast block LMS. These algorithms were applied to decompose the measured vibration signal into deterministic and random parts with the latter containing the bearing signal. These techniques were applied to identify a bearing fault in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison with the other algorithms.

A modified frequency-domain block LMS algorithm with guaranteed optimal steady-state performance

The bin-normalized frequency-domain block LMS (FBLMS) algorithm has low computational burden and potential fast convergence; however, it suffers from a biased steady-state solution when the reference signal lags behind the desired signal or the adaptive filter is of insufficient length. This paper proposes a unified framework for the FBLMS algorithm, which can be used to comprehensively analyze its steady-state behavior. Furthermore, a modified FBLMS algorithm with guaranteed optimal steady-state performance is proposed based on the framework. Simulations are carried out to demonstrate the benefit of the proposed algorithm.