Standard Adaptive Algorithms Research Articles

Improved performance of impulse active noise control using active function threshold with absolute harmonic variable step-size algorithm

The Standard (conventional) adaptive algorithms exhibits low convergence rate and minimum noise suppression, or else the system becomes unstable under Gaussian and non-Gaussian (impulsive noise SαS distributions) noise environments. In order to overcome the drawback of traditional algorithms (i.e., to eliminate unwanted noise), the popular algorithm Filtered Cross Minimum Square (FxLMS) is used in Active Noise Control (ANC), not only to improve its efficiency but also to improve its performance. In this paper, we proposed two improvements: first, we proposed a novel method Active threshold function FxLMS (ATFxLMS) being employed in ANC in the paths of primary (reference) and error signals; a second proposal is employing the Variable Step-Size based on Absolute Harmonic Mean (AHMVSS) of error signal. The idea behind this method is that the step-size of the algorithm varies depending on the harmonic mean of error signal obtained from the error location. In comparison to the fixed step-size algorithm, the proposed ATF-AHMVSS provided an improved convergence rate for the desired ANC efficiency. Moreover computational complication of the proposed method was examined as it was found that the proposed algorithm provided stable condition for ANC systems. Computer simulation results are revealed that the proposed (AT & AHMVSS-FxLMS) algorithm have attained excellent performance in terms of convergence speed, noise reduction and minimum steady state error as compared to other existing algorithms under different noise inputs. The results obtained from the proposed algorithm show outperformance compared to traditional adaptive algorithms.

Analysis of Acoustic Feedback Cancellation Systems based on Direct Closed-Loop Identification

This work presents, using the least squares estimation theory, a theoretical and experimental analysis on the performance of the standard adaptive filtering algorithms when applied to acoustic feedback cancellation. Expressions for the bias and covariance matrix of the acoustic feedback path estimate provided by these algorithms are derived as a function of the signals statistics as well as derivatives of the cost function. It is demonstrated that, in general, the estimate is biased and presents a large covariance because the closed-loop nature of the system makes the cross-correlation between the loudspeaker and system input signals non-zero. Simulations are carried out to exemplify the results using speech signals, a long acoustic feedback path and the recursive least squares algorithm. The results illustrate that these algorithms converge very slowly to a solution that is not the true acoustic feedback path. The relationship between the performance of the adaptive filtering algorithms and the aforementioned cross-correlation is proven by varying the signal-to-noise ratio and the delay introduced by the forward path.

Robust Optimization Framework for Proactive User Association in UDNs: A Data-Driven Approach

We study the user association problem in the context of dense networks, where standard adaptive algorithms become ineffective. This paper proposes a novel data-driven technique leveraging the theory of robust optimization. The main idea is to predict future traffic fluctuations, and use the predictions to design association maps before the actual arrival of traffic. Although, the actual playout of the map is random due to prediction error, the maps are robustly designed to handle uncertainty, preventing constraint violations, and maximizing the expectation of a convex utility function, which is used to accurately balance base station loads. We propose a generalized iterative algorithm, referred to as GRMA, which is shown to converge to the optimal robust map. The optimal maps have the intriguing property that they jointly optimize the predicted load and the variance of the prediction error. We validate our robust maps in Milano-area traces, with dense coverage and find that we can reduce violations from 25% (inflicted by a baseline adaptive algorithm) down to almost zero.

An adaptive algorithm, based on modified tanh non-linearity and fractional processing, for impulsive active noise control systems

This paper presents an adaptive algorithm for active control of noise sources that are of impulsive nature. The impulsive type sources can be better modeled as a stable distribution than the Gaussian. However, for stable distributions, the variance (second order moment) is infinite. The standard adaptive filtering algorithms, which are based on minimizing variance and assuming Gaussian distribution, converge slowly or become even unstable for stable (impulsive) processes. In order to improve the performance of the standard filtered-x least mean square (FxLMS)-based impulsive active noise control (IANC) systems, we propose two enhancements in this paper. First, we propose employing modified tanh function-based nonlinear process in the reference and error paths of the standard FxLMS algorithm. The main idea is to automatically give an appropriate weight to the various samples in the process, i.e. appropriately threshold the very large values so that system remains stable, and give more weight to samples below threshold limit so that the convergence speed can be improved. A second proposal is to incorporate the fractional-gradient computation in the update vector of IANC adaptive filter. Computer simulations have been carried out using experimental data for the acoustic paths. The simulation results demonstrate that the proposed algorithm is very effective for IANC systems.

Performance Analysis of Adaptive Algorithms for Speech Enhancement Applications

Background/Objectives: The improvement of the speech quality is an essential component in this modern world. Though different methods have been used by many researchers since a long period, still the research field is open to work further. In this paper, we have analyzed the same using adaptive algorithms. Methods/Statistical Analysis: The algorithms like Least Mean Square (LMS) and Recursive Least Square (RLS) are used to improve the quality. Further, State-Space Recursive Least Square (SSRLS) algorithm is developed for enhancement of the noisy speech. The existing Spectral Subtraction (SS) algorithm is verified for two types of noisy speech. Also the standard adaptive algorithms have been experienced for these signals. Findings: Spectral Subtraction method is implemented initially. Then LMS and RLS algorithms are tested for two different types of noisy speech signals. In case of LMS, the improvement of SNR is 8.2822 dB for random noise and 6.5245 dB for babble noise. Whereas for RLS algorithm, the SNR improvement is 11.7152 dB for random noise and 14.3883dB for babble noise. The SNR after enhancement is 43.5409 dB and 47.2456 dB for random noise and for babble noise respectively in case of SSRLS algorithm which is much higher than other algorithms as results suggest. Also the error minimization curve converges more rapidly than the others. So SSRLS algorithm provides better enhanced signal than the other three methods. Application/Improvements: The applications are in different field of communications like mobile telephony, video conferences as audio and video applications. Also the applications are of speech recognition, voice communications, information forensics and many more. The improvement of SNR in this proposed algorithm found better than SS, LMS and RLS algorithms as 31.2377 dB and 35.6381 dB.

Adaptive beamformer based on the augmented complex least mean square algorithm

ABSTRACTIn this article, an adaptive beamforming system based on the augmented complex least mean square algorithm is analysed. In this approach, the adaptive filter is used as a widely linear system. The second-order statistical information of the signals involved in the array is exploited. Under this consideration, the ability of the adaptive array to minimize the effects of interferences and complex white noise could be enhanced. The equations for the optimal weights and the array factor are derived for the proposed beamforming system. Computer simulations have been performed to evaluate the performance of the adaptive array, and the results were compared with two of the most common standard adaptive beamforming algorithms: the least mean square and recursive least square. The numerical simulations show that the proposed adaptive array has a better performance in time and spatial domain as compared to the classical beamforming systems.

Stability of adaptive control in the presence of input disturbances and H∞. performance

Adaptive control in the presence of input disturbances is considered. It is shown that standard adaptive control algorithms can stabilize the system in the presence of input disturbances and guarantee an ℒ2-gain bound. This method allows one to guarantee the performance of the adaptive control even before the adaptive parameters converge to their optimal values. The results are obtained for linear systems with polytopic type parameter uncertainties. Static output-feedback as well as state feedback are considered. The computational efficiency of the results is also discussed. An illustrative example is given.

Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems

In the present study, single and two-stage least mean square (LMS) adaptive strategies based on fractional signal processing are developed for parameter estimation of controlled autoregressive moving average (CARMA) systems. The main idea is to use fractional LMS identification (FLMSI) and two-stage FLMSI (TS-FLMSI) algorithms for CARMA model that is decomposed into a system and noise models. The performance analyses for both proposed FLMSI and TS-FLMSI schemes are conducted based on adapting the prior known design parameters of the system and comparing the results with standard adaptive algorithms. The accuracy and convergence of the design schemes are verified and validated through the results of statistical analyses based on sufficient number of independent runs to adapt CARMA system. Comparative studies established the dominance of single and two-stage fractional adaptive algorithms over other counterpart in term of model accuracy and reliability in case of different scenarios based on variant signal to noise ratios and step size parameters.

Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems

High-dimensional inverse problems present a challenge for Markov chain Monte Carlo (MCMC)-type sampling schemes. Typically, they rely on finding an efficient proposal distribution, which can be difficult for large-scale problems, even with adaptive approaches. Moreover, the autocorrelations of the samples typically increase with dimension, which leads to the need for long sample chains. We present an alternative method for sampling from posterior distributions in nonlinear inverse problems, when the measurement error and prior are both Gaussian. The approach computes a candidate sample by solving a stochastic optimization problem. In the linear case, these samples are directly from the posterior density, but this is not so in the nonlinear case. We derive the form of the sample density in the nonlinear case, and then show how to use it within both a Metropolis--Hastings and importance sampling framework to obtain samples from the posterior distribution of the parameters. We demonstrate, with various small- and medium-scale problems, that randomize-then-optimize can be efficient compared to standard adaptive MCMC algorithms.

Comparative Performance Investigations of Stochastic and Genetic Algorithms Under Fast Dynamically Changing Environment in Smart Antennas

In a mobile communication systems, the number of observation data (snapshots) used for covariance matrix estimation can be insufficient, which often occurs due to fast dynamically changing environment or signal characteristics are rapidly changing. In these situations, the performance of the standard adaptive algorithms such as LMS are known to degrade substantially. In this paper, we propose the use of a Genetic Algorithm (GA) to perform the adaptation control of the system parameters under dynamically changing environments The GA-based beamformer has nearly optimal interference cancellation under dynamic conditions, and makes the output SINR consistently close to the optimal one regardless of the number of snapshot used. Other advantages of the GA is its simplicity and fast convergence provided that the parameters are appropriately chosen, which makes it a practical algorithm for beamforming in smart antenna. Simulation results validate substantial performance improvements relative to other standard adaptive algorithms. Although, the use of GA is not new in smart antenna technology, the performance evaluation of the genetic optimization under fast dynamically changing environment has not been investigated to the best of my knowledge and it is of great practical significance. DOI: http://dx.doi.org/10.11591/ijece.v2i1.119

Proportionate-Type Normalized Least Mean Square Algorithms With Gain Allocation Motivated by Mean-Square-Error Minimization for White Input

In the past, ad hoc methods have been used to choose gains in proportionate-type normalized least mean-square algorithms without strong theoretical under-pinnings. In this correspondence, a theoretical framework and motivation for adaptively choosing gains is presented, such that the mean-square error will be minimized at any given time. As a result of this approach, a new optimal proportionate-type normalized least mean-square algorithm is introduced. A computationally simplified version of the theoretical optimal algorithm is derived as well. Both of these algorithms require knowledge of the mean-square weight deviations. Feasible implementations, which estimate the mean-square weight deviations, are presented. The performance of these new feasible algorithms are compared to the performance of standard adaptive algorithms when operating with sparse, non-sparse, and time-varying impulse responses, when the input signal is white. Specifically, we consider the transient and steady-state mean-square errors as well as the overall computational complexity of each algorithm.

Optimally regularized adaptive filtering algorithms for room acoustic signal enhancement

In many room acoustic signal processing applications, a room impulse response identification is needed to eliminate undesired effects such as echo, feedback, or reverberation. This is typically done using an adaptive filter driven by a speech or audio input signal. However, such signals exhibit poor excitation properties, which cause standard adaptive filtering algorithms to be very sensitive to disturbing signals, especially in the underdetermined case. A popular remedy is regularization, which is usually implemented with a scaled identity regularization matrix. This type of regularization is governed by a single regularization parameter, the value of which is often chosen in an arbitrary way. We propose to regularize the adaptive filter using a non-identity regularization matrix, in which prior knowledge on the unknown room impulse response may be incorporated. When knowledge of the disturbing signal is also used to add prefiltering and weighting in the adaptation, a new family of regularized adaptive filtering algorithms is obtained, which is shown to be optimal in a mean square error sense. Existing regularized algorithms can then be obtained as special cases, assuming limited or no prior knowledge is available. When combined with a recently proposed method of extracting prior knowledge from the acoustic setup, our algorithms exhibit superior convergence behaviour compared to existing algorithms in different simulation scenarios, while the additional computational cost is small.

Adaptive algorithms for sparse echo cancellation

The cancellation of echoes is a vital component of telephony networks. In some cases the echo response that must be identified by the echo canceller is sparse, as for example when telephony traffic is routed over networks with unknown delay such as packet-switched networks. The sparse nature of such a response causes standard adaptive algorithms including normalized LMS to perform poorly. This paper begins by providing a review of techniques that aim to give improved echo cancellation performance when the echo response is sparse. In addition, adaptive filters can also be designed to exploit sparseness in the input signal by using partial update procedures. This concept is discussed and the MMax procedure is reviewed. We proceed to present a new high performance sparse adaptive algorithm and provide comparative echo cancellation results to show the relative performance of the existing and new algorithms. Finally, an efficient low cost implementation of our new algorithm using partial update adaptation is presented and evaluated. This algorithm exploits both sparseness of the echo response and also sparseness of the input signal in order to achieve high performance without high computational cost.

Filterbank-based blind code synchronization for ds-cdma systems in multipath fading channels

We present a filterbank approach to blind code synchronization for asynchronous direct-sequence (DS) code-division multiple-access (CDMA) systems. The key idea of the proposed scheme is to first pass the received signal through a bank of filters, which are designed to enhance signals of interest and suppress interference/noise, and then to derive the code timing from the filtered data. The only required knowledge by the proposed filterbank scheme is the spreading code of the desired user. It can be used in various environments, including frequency-nonselective and frequency-selective, time-invariant, and time-varying fading channels. It can deal with colored channel noise and unmodeled interference, such as inter-cell interference (ICI) and narrowband interference. It has relatively low complexity and can be readily implemented using standard adaptive algorithms. We show that under mild conditions, the proposed scheme yields statistically consistent [in signal-to-noise ratio (SNR)] code timing estimates, irrespective of the strength of the interference and with only a finite number of data samples. We also derive an unconditional Cramer-Rao bound (UCRB), which serves as a lower bound for all unbiased blind code synchronization schemes. Numerical results indicate that the proposed scheme compares favorably with a popular subspace-based method in terms of user capacity, near-far resistance, and robustness to time-varying fading and unmodeled interference.

Nonlinearities enhance parameter convergence in output-feedback systems

While the parameter convergence properties of standard adaptive algorithms for linear systems are well established, there are no similar results on the parameter convergence of adaptive controllers for nonlinear systems which have gained popularity in recent years. In this paper we focus on a recently developed class of adaptive schemes for output-feedback nonlinear systems and show that parameter convergence is guaranteed if and only if an appropriately defined signal vector, which does not depend on closed-loop signals, is persistently exciting. Then we develop an analytic procedure which allows us, given a specific nonlinear system and a specific reference signal, to determine a priori whether or not this vector is persistently exciting (PE) and, hence, whether or not the parameter estimates will converge. In the process we show that the presence of nonlinearities usually reduces the sufficient richness (SR) requirements on the reference signals and hence enhances parameter convergence. This is the first result on the relationship between PE and SR for adaptive nonlinear control systems.

Issues in the implementation of an indirect adaptive control system

This work presents techniques for addressing the various issues relevant to the implementation of an indirect adaptive control system. The sufficiency of these techniques is demonstrated by implementation onboard a small autonomous underwater vehicle. The control system is organized into two layers: the standard adaptive control algorithms and the supervisory logic. Modeling and implementation issues involved in the design of the basic adaptive control loop are described. The design of supervisory logic to address insufficiency of excitation, identification under low signal-to-noise ratio conditions, and guaranteeing appropriate control actions during periods when the parameter estimates are inaccurate is discussed. Results from in-water demonstrations are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Stochastic Adaptive Control for Time-varying Systems Convergent at the Rate of 1/t

In this paper, it is shown that the standard adaptive control algorithms for time-invariant systems have an inherent robustness properties which makes them applicable. without modification, to time-varying systems whose parameters convergent at the rate of 1/t . it is proved that a system which is convergent at the rate of 1/t towords a strictly passive system is eventually strictly passive in a certain sense.Then we show the global convergence for the discrete time stochastic adaptive variance control when the system parameters are convergent at the rate of lit to values satisfying the conditions for globally convergent time-invariant system. Some properties of the systems on strong consistency are studied