Least Mean Square Adaptive Algorithm Research Articles

ADAPTIVE BEAMFORMING USING LMS ALGORITHM

The smart antennas are widely used for wireless communication, because it has a ability to increase the coverage and capacity of a communication system. Smart antenna performs two main functions such as direction of arrival estimation (DOA) and beamforming. Using beamforming algorithm . smart antenna is able to form main beam towards desired user and null in the direction of interfering signals. This paper evaluate the performance of LMS (Least Mean Square) beamforming algorithm in the form of normalized array factor (NAF) and mean square error(MSE) by varying the number of elements in the array and the placing between the sensor elements. The simulations are carried out using MATLAB.

Critical-Path Analysis and Low-Complexity Implementation of the LMS Adaptive Algorithm

This paper presents a precise analysis of the critical path of the least-mean-square (LMS) adaptive filter for deriving its architectures for high-speed and low-complexity implementation. It is shown that the direct-form LMS adaptive filter has nearly the same critical path as its transpose-form counterpart, but provides much faster convergence and lower register complexity. From the critical-path evaluation, it is further shown that no pipelining is required for implementing a direct-form LMS adaptive filter for most practical cases, and can be realized with a very small adaptation delay in cases where a very high sampling rate is required. Based on these findings, this paper proposes three structures of the LMS adaptive filter: (i) Design 1 having no adaptation delays, (ii) Design 2 with only one adaptation delay, and (iii) Design 3 with two adaptation delays. Design 1 involves the minimum area and the minimum energy per sample (EPS). The best of existing direct-form structures requires 80.4% more area and 41.9% more EPS compared to Design 1. Designs 2 and 3 involve slightly more EPS than the Design 1 but offer nearly twice and thrice the MUF at a cost of 55.0% and 60.6% more area, respectively.

The Use of LMS and RLS Adaptive Algorithms for an Adaptive Control Method of Active Power Filter

This paper deals with the adaptive control mechanism management meant for shunt active power filters (SAPF). Systems driven this way are designed to improve the quality of electric power (power quality) in industrial networks. The authors have focused on the implementation of two basic representatives of adaptive algorithms, first, the algorithm with a stochastic LMS (least mean square) gradient adaptation and then an algorithm with recursive RLS (recursive least square) optimal adaptation. The system examined by the authors can be used for non-linear loads for appliances with rapid fluctuations of the reactive and active power consumption. The proposed system adaptively reduces distortion, falls (dip) and changes in a supply voltage (flicker). Real signals for measurement were obtained at a sophisticated, three-phase experimental workplace. The results of executed experiments indicate that, with use of the certain adaptive algorithms, the examined AHC system shows very good dynamics, resulting in a much faster transition during the AHC connection-disconnection or during a change in harmonic load on the network. The actual experiments are evaluated from several points of view, mainly according to a time convergence (convergence time) and mistakes in a stable state error (steady state error) of the investigated adaptive algorithms and finally as a total harmonic distortion (THD). The article presents a comparison of the most frequently used adaptive algorithms.

Chaotic characteristic analysis for traffic flow series and DFPSOVF prediction model

Time delay and optimal embedding dimension for the real measurement traffic flow series, which are used by mutual information method and false nearest-neighbor method, respectively, are determined for phase space reconstruction of the traffic flow series. The saturation correlation dimension and the largest Lyapunov exponent for traffic flow series are calculated to estimate its chaotic characteristics. Based on the least mean square (LMS) algorithm, a novel second-order Volterra model using Davidon-Fletcher-Powell method (DFPSOVF) is constructed, in which a variable convergence factor based on a posteriori error assumption, characteristic of real-time change with the input signal, is applied. DFPSOVF model can avoid some problems caused by improper selection of parameters when using LMS adaptive algorithm for coefficient adjustment in Volterra model. DFPSOVF model can also be applied to short-term traffic flow prediction with chaotic characteristics. Results show that when model memory length is consistent with embedding dimension of traffic flow series, it obtains higher prediction accuracy, which can meet the needs for traffic guidance and traffic control, and can also provide a new method, a new idea and engineering application reference for intelligent traffic control.

Performance analysis of under-modelling stereophonic acoustic echo cancellation by adaptive filtering LMS algorithm

This paper addresses the field of stereophonic acoustic echo cancellation (SAEC) with adaptive filtering algorithms. In SAEC applications, using the least mean square (LMS) algorithm, it is usually assumed that the lengths of the adaptive filters are equal to that of the unidentified system responses. Although, in many realistic situations, under-modelled lengths adaptive filters, whose lengths are less than that of the unidentified systems (under-modelled systems), are employed, and analysis results for the exact modelled stereophonic LMS algorithm are not automatically appropriate to the under-modeled lengths. In this paper, we present a statistical analysis of the under-modeled stereophonic LMS algorithm. Exact expressions and deterministic recursive equations to the mean coefficients behavior of the adaptive LMS filters are derived to completely characterize and assess the performances (transient and steady-state) of the under-modeling stereophonic LMS algorithm. The expected theoretical behaviour is compared with Monte Carlo simulations and practical experimental results, showing a very good agreement.

Complex Adaptive LMS Algorithm Employing the Conjugate Gradient Principle for Channel Estimation and Equalization

The Complex Block Least Mean Square (LMS) technique is widely used in adaptive filtering applications because of its simplicity and efficiency from a theoretical and implementation standpoint. However, the limitations of the Complex Block LMS technique are slow convergence and dependence on the proper choice of the stepsize or convergence factor. Moreover, its performance degrades significantly in time-varying environments. In this paper, a novel adaptive LMS technique named the Complex Block Conjugate LMS algorithm, CBC-LMS, is presented. Based on the Conjugate Gradient Principle, the proposed technique searches orthogonal directions to update the filter coefficients instead of the negative gradient directions used in the Complex Block LMS algorithm. In addition, the CBC-LMS algorithm derives optimal stepsizes to adjust the adaptive system coefficients at each iteration. As a result, the developed method overcomes the inherent limitations of the existing Complex Block LMS algorithm. The performance of the CBC-LMS technique is tested in wireless channel estimation and equalization applications, using both computer simulations and laboratory experiments. Furthermore, the developed technique is compared to the Complex Block LMS method and a recently proposed method, which is called Complex Optimal Block Adaptive LMS (OBA-LMS). The experimental and simulation results confirm that the proposed CBC-LMS technique achieves faster convergence with comparable accuracy and reduced computational complexity, relative to the existing techniques.

Simulation and Comparison of Adaptive Detection Algorithm

The active power filter has two key link for harmonic current detection and respectively compensation current tracking. This article mainly aims at harmonic current detection link. Firstly, Analysis adaptive harmonic detection methods, then based on over to LMS (Least Mean Square) adaptive algorithm as the research object, in discussing the adaptive algorithm criteria. By comparing several simulation-based LMS algorithm to achieve the results of the filter,analyse the causes of the results.

Amplitude phase control for electro-hydraulic servo system based on normalized least-mean-square adaptive filtering algorithm

The electro-hydraulic servo system was studied to cancel the amplitude attenuation and phase delay of its sinusoidal response, by developing a network using normalized least-mean-square (LMS) adaptive filtering algorithm. The command input was corrected by weights to generate the desired input for the algorithm, and the feedback was brought into the feedback correction, whose output was the weighted feedback. The weights of the normalized LMS adaptive filtering algorithm were updated on-line according to the estimation error between the desired input and the weighted feedback. Thus, the updated weights were copied to the input correction. The estimation error was forced to zero by the normalized LMS adaptive filtering algorithm such that the weighted feedback was equal to the desired input, making the feedback track the command. The above concept was used as a basis for the development of amplitude phase control. The method has good real-time performance without estimating the system model. The simulation and experiment results show that the proposed amplitude phase control can efficiently cancel the amplitude attenuation and phase delay with high precision.

Performance Comparison of STFT, WT, LMS and RLS Adaptive Algorithms in Denoising of Speech Signal

Different types of noise cancellation techniques are prevalent in recent literatures. The performance of a particular technique depends on mean, variance and maximum amplitude of error. At the same time the process time of signal and complexity of practical implementation of circuits is also a measuring tool for performance of a technique. The objective of this paper is to compare performance among the short time Fourier transform (STFT), wavelet transform (WT), least mean square (LMS) and recursive least square (RLS) methods in cancellation of noise from a speech signal. The analysis of the paper provides us the way of selection of the best denoising technique based on the statistical parameters of the above four mentioned techniques. Index Terms—Signal denoising, CPU time, statistical parameters of adaptive filter, wavelet transform, short-time Fourier transform, least mean square and recursive least square.

적응 간섭 제거 시스템을 위한 상관도를 적용한 적응적 궤환 간섭 제거 알고리즘

셀룰러 시스템에서의 음영 지역 해소 및 전송 용량 증대를 위해 중계기의 중요성은 계속적으로 증가하고 있다. 그러나 RF 중계기는 중계된 전송 신호의 일부가 궤환되어 다시 수신 안테나에 수신되는 궤환 간섭 신호가 발생한다. 이런 문제를 해결하기 위하여 본 논문에서는 RF 중계기의 성능 개선을 위해 상관도를 적용한 Sign-Sign LMS(Least Mean Square)를 제안하였다. 기존 알고리즘의 제곱된 에러를 최소화하기 위해 가중치 벡터는 입력 신호와 오차 신호의 부호에 취하고, 이를 활용하여 갱신된다. 제안된 간섭 제거기는 기존 방식과 비교하여 MSE(Mean Square Error) 측면에서 최대 10 dB의 성능 이득을 가진다. To reduce the outage probability and to increase the transmission capacity, the importance of repeaters in cellular systems is increasing. But a RF(Radio Frequency) repeater has a problem that the output of the transmit antenna is partially feedback to the receive antenna, which is feedback interference. In this paper, we proposed adaptive Sign-Sign LMS(Least Mean Square) algorithm using correlations for the performance enhancement of RF repeater. The weight vector is updated by using sign of input signal and error signal to the least squared error of the conventional algorithms. When compared with the conventional method, the proposed canceller achieves the maximum 10 dB performance gain in terms of the MSE(Mean Square Error).

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters

In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory.

Statistical analysis of the LMS adaptive algorithm subjected to a symmetric dead-zone nonlinearity at the adaptive filter output

This work presents a statistical analysis of the Least Mean Square (LMS) adaptive algorithm subjected to the existence of a symmetric dead-zone nonlinearity at the adaptive filter output. Such configuration can be representative of low-cost active noise control systems where the canceling signal drives a class B power amplifier or a nonlinear actuator. Recursive deterministic equations are derived for the mean coefficient and mean-square error behaviors assuming Gaussian signals and slow adaptation. The steady-state algorithm behavior is determined for given filter parameters and degree of nonlinearity. Monte Carlo simulations and laboratory experiments are presented which corroborate the theoretical results.

Adaptive precision positioning of smart composite panels subjected to external disturbances

This paper addresses the simultaneous precision positioning and vibration suppression of smart composite panels with piezoelectric patches. The filtered-x least mean square (LMS) adaptive feedforward control (AFC) for vibration/noise reduction is extended for this purpose, and AFCs with feedback loops are developed. The introduced feedback loops enhance the computational efficiency of the adaptive LMS algorithm and vibration suppression capability. Besides the classic PID feedback loop, the internal model configuration is introduced as well for the case in which the external disturbance is unknown. Experiments are performed on an active composite panel under a harmonic or random disturbance. The effects of the convergence factors of the adaptive algorithm on the overshoot and settling time of the position response of the smart composite panel are investigated. The vibration suppression and precision positioning capabilities of the AFC and the AFC with feedback loops are compared and discussed. The experimental results demonstrate that the overall positioning and vibration suppression performances for the AFC with feedback loops are better than those for the AFC.

Performance analysis of the deficient length LMS adaptive algorithm

In almost all analyzes of the least mean-square (LMS) finite impulse response (FIR) adaptive algorithm, it is assumed that the length of the adaptive filter is equal to that of the unknown system impulse response. However, in many practical situations, a deficient length adaptive filter, whose length is less than that of the unknown system, is employed, and analysis results for the sufficient length LMS algorithm are not necessarily applicable to the deficient length case. Therefore, there is an essential need to accurately quantify the behavior of the LMS algorithm for realistic situations where the length of the adaptive filter is deficient. In this paper, we present a performance analysis for the deficient length LMS adaptive algorithm for correlated Gaussian input data and using the common independence assumption. Exact expressions that completely characterize the transient and steady-state mean-square performances of the algorithm are developed, which lead to new insights into the statistical behavior of the deficient length LMS algorithm. Simulation experiments illustrate the accuracy of the theoretical results in predicting the convergence behavior of the algorithm.

FPGA Implementation of a Single Channel GPS Interference Mitigation Algorithm

The FPGA (Field-Programmable Gate Array) implementation of an adaptive filter for narrow band interference excision in Global Positioning Systems is described. The algorithm implemented is a delayed LMS (Least Mean Squares) adaptive algorithm improved by incorporating a leakage factor, rounding and constant resetting of the filter weights. This was necessary as the original adaptive algorithm had stability problems : the filter weights did not remain fixed, and tended to drift until they overflowed, causing the filter response to degrade. Each model was first tested in Simulink, implemented in VHDL (Verilog Hardware Description Language) and then downloaded to an FPGA board for final testing. Experimental measurements of anti-jam margins were obtained.

Sound equalization in a large region of a rectangular enclosure (L)

The work presented by Santillán [J. Acoust. Soc. Am. 110, 1989–1997 (2001)] about equalization at low frequencies in rectangular enclosures is extended, and topics that remained unaddressed in the original study are treated in this paper. A modification is introduced to the original cost function that leads to a least-squares problem formulation that demonstrates increased robustness, and the multiple-error LMS (Least Mean Square) adaptive algorithm is employed to approximate the coefficients of the equalization filters to their optimum values. Other issues studied in this paper include the dependence of the limits of the zone of equalization on factors such as the damping constant of the modes of the enclosure, the number of sources, and the driving frequency.

Analysis of mean-square error and transient speed of the LMS adaptive algorithm

For the least mean square (LMS) algorithm, we analyze the correlation matrix of the filter coefficient estimation error and the signal estimation error in the transient phase as well as in steady state. We establish the convergence of the second-order statistics as the number of iterations increases, and we derive the exact asymptotic expressions for the mean square errors. In particular, the result for the excess signal estimation error gives conditions under which the LMS algorithm outperforms the Wiener filter with the same number of taps. We also analyze a new measure of transient speed. We do not assume a linear regression model: the desired signal and the data process are allowed to be nonlinearly related. The data is assumed to be an instantaneous transformation of a stationary Markov process satisfying certain ergodic conditions.

Fast implementations of the filtered-X LMS and LMS algorithms for multichannel active noise control

In some situations where active noise control could be used, the well-known multichannel version of the filtered-X least mean square (LMS) adaptive filter is too computationally complex to implement. We develop a fast, exact implementation of this adaptive filter for which the system's complexity scales according to the number of filter coefficients within the system. In addition, we extend computationally efficient methods for effectively removing the delays of the secondary paths within the coefficient updates to the multichannel case, thus yielding fast implementations of the LMS adaptive algorithm for multichannel active noise control. Examples illustrate both the equivalence of the algorithms to their original counterparts and the computational gains provided by the new algorithms.

Comparison of wideband LMS, subband LMS, and a nonlinear entropic neural approach to adaptive noise cancellation for speech enhancement

An empirical comparison of three adaptive algorithms for speech enhancement in noisy reverberant conditions is presented. The subband least mean square (LMS) [E. Toner and D. R. Campbell, Speech Commun. 12, 253–259 (1993)] and minimum entropy noise reduction schemes [M. Girolami, Electron. Lett. 33(17), 1437–1438 (1997)] are compared with a reference wideband LMS approach [S. D. Stearns and R. A. David, Signal Processing Algorithms (Prentice–Hall, Englewood Cliffs, NJ, 1988)]. All three methods were applied to the enhancement of speech corrupted with speech-shaped noise. The schemes were tested using simulated anechoic and real-room reverberant (T60=0.3 s) environments. The anechoic and real-room recordings were binaural including head shadow and diffraction effects. The subband LMS adaptive processing scheme uses the LMS adaptive noise cancellation algorithm in frequency-limited subbands. The inputs from each microphone are split into 16 contiguous subbands using a cochlear distribution according the function provided by Greenwood [J. Acoust. Soc. Am. 87, 2592–2605 (1990)]. Each frequency-limited subband is processed using a LMS adaptive noise cancellation filter operating in an intermittent or continuous mode depending on input signal characteristics. The neural network approach is motivated by temporally sensitive hebbian super-synapses, which may form sparse representations based on commonly occurring spatio-temporal input patterns. The results for real-room signals demonstrate statistically equivalent performance of the subband LMS and wideband neural method, both of which prove superior to the wideband LMS algorithm.

Architectural Synthesis of Computational Engines for Subband Adaptive Filtering

Architectural synthesis of low-power computational engines (hardware accelerators) for a subband-based adaptive filtering algorithm is presented. The full-band least mean square (LMS) adaptive filtering algorithm, widely used in various applications, is confronted by two problems, viz., slow convergence when the input correlation matrix is ill-conditioned, and increased computational complexity for applications involving use of large adaptive filter orders. Both of these problems can be overcome by the use of a subband-based normalized LMS (NLMS) adaptive filtering algorithm. Since this algorithm is not amenable to pipelining, delayed coefficient adaptation in the NLMS updation is used, which provides the required delays for pipelining. However, the convergence speed of this subband-based delayed NLMS (DNLMS) algorithm degrades with increase in the adaptation delay. We first present a pipelined subband DNLMS adaptive filtering architecture with minimal adaptation delay for any given sampling rate. The architecture is synthesized by using a number of function preserving transformations on the signal flow graph (SFG) representation of the subband DNLMS algorithm. With the use of carry-save arithmetic, the pipelined architecture can support high sampling rates limited only by the delay of two full adders and a 2-to-1 multiplexer. We then extend this synthesis methodology to synthesize a pipelined subband DNLMS architecture whose power dissipation meets a specified budget. This low-power architecture exploits the parallelism in the subband DNLMS algorithm to meet the required computational throughput. The architecture exhibits a novel tradeoff between algorithmic performance (convergence speed) and power dissipation. Finally, we incorporate configurability for filter order, sample period, power reduction factor, number of subbands and decimation/interpolation factor in the low-power architecture, thus resulting in a low-power subband computational engine for adaptive filtering.