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In this paper, we present a novel adaptive tap algorithm for partial update adaptive filters used in network echo cancellation. As the channel is typically long and sparse, it is unnecessary and inefficient to update all of the taps. Although partial update algorithms can be used to solve this problem, it is difficult to predetermine a fixed number of partial-update taps without a priori knowledge...
In this paper, a new variable step size method for online acoustic feedback path modeling (and neutralization) in single channel active noise control (ANC) system is proposed. The proposed method uses three filters. (1) A least mean square based disturbance estimation filter (2) A variable step size LMS based feedback path modeling filter and (3) An FxLMS based noise control filter. In the proposed...
Adaptive filtering is used in a wide range of applications including echo cancellation, noise cancellation and equalization. In these applications, the environment in which the adaptive filter operates is often non-stationary. For satisfactory performance under non-stationary conditions, an adaptive filtering is required to follow the statistical variations of the environment. Tracking analysis provides...
The total energy consumed in a wireless sensor network until reaching a consensus is proportional to the product of the sum of the transmission power of each node and the convergence time. In a network where the nodes are allowed to transmit using different power levels, the minimization of this consumption is not trivial. In this paper, we propose a heuristic scheme of randomized power transmission...
We investigate the probabilistic convergence behavior of minimum mean square error (MMSE) turbo equalization in space-time (ST) block-coded multiple-input multiple-output (MIMO) systems with finite block lengths. For this purpose, the extrinsic information transfer characteristics (EXIT)-band chart technique, which was originally proposed for analyzing turbo decoding of parallel concatenated codes...
Orthogonal Frequency Division Multiplexing (OFDM) is seen as one of the most promising solution to broadband wireless communications. Its performance depends on the channel state information (CSI) which can be estimated using different channel estimation algorithms. This paper proposes a Set Member Feasibility (SMF) formulation to govern the updating of the adaptive-filter coefficients. Here, two-dimensional...
In this paper Normalized Kernel Least Mean Square (NKLMS) algorithm is presented which has applications in system modeling and pattern recognition. In 2007 a similar algorithm was proposed Named Kernel Least Mean Square (KLMS), and a modified version of KLMS was introduced in 2008. Although KLMS has good results in prediction of some time series, high sensitivity to step-size and signal amplitude...
Control channel model parameters of adaptive filter feedforward control for piezoelectric smart flexible structures should be predicted, as not only they are the key factors of system stability and control effect but also they have immediate impacts on system convergence and the validity of control strategy. This paper takes the piezoelectric flexible structure as research object. According to least...
We consider linear prediction problems in a stochastic environment. The least mean square (LMS) algorithm is a well-known, easy to implement and computationally cheap solution to this problem. However, as it is well known, the LMS algorithm, being a stochastic gradient descent rule, may converge slowly. The recursive least squares (RLS) algorithm overcomes this problem, but its computational cost...
The conventional algorithms in the echo canceling system have drawback when they are faced with double-talk condition in noisy environment. Since the double-talk and noise signal are exist, then the error signal is contaminated to estimate the gradient correctly. In this paper, we define a new adaptive algorithm for tap adaptations, based on the correlation function processing, which is called Extended...
As there is a conflict about block step size between the requirements of the convergence velocity and parameters' maladjustment within FBLMS algorithm, a modified FBLMS algorithm is well presented in this paper which uses a variable block step size to solve the contradiction between convergence rate and precision. The modified FBLMS algorithm is simulated in the MATLAB platform. The simulation results...
This paper presents a flexible method of achieving either fixed or self-adaptive antenna beamforming. It involves the use of the array image factor A??d, which interfaces the Recursive Least Squares (RLS) and Least Mean Squares (LMS) sections in cascade to form the RLMS beamforming algorithm. It is shown that an accurate fixed beam can be obtained by simply setting the elements of A??d A with prescribed...
There are many interference canceller methods widely used in the coexisting Bluetooth and WLAN systems recent years. Among these methods, an adaptive filter followed by Rake receiver system is simple and efficiency. And there are also many algorithms used in adaptive filter being researched. Combining with these different algorithms, the system can improve its efficiency further. We proposed a modified...
For the subband adaptive filtering has the better performance in convergence and computing efficiency, it has been widely used in many signal processing fields, but the aliasing in-band from decimated in subband impair the system performance greatly. In the paper, based on the theory of signal orthogonal decomposition, used self-contained sinusoid basis, a novel subband signal adaptive noise cancellation...
The usefulness of persistent excitation is well-known in the control community. Thanks to a persistently excited adaptive tracking control, we show that it is possible to avoid the strong controllability assumption recently proposed in the multidimensional ARX framework. We establish the almost sure convergence for both least squares and weighted least squares estimators of the unknown parameters...
This paper derives an adaptation algorithm named least mean modulus-Newton (LMM-Newton) algorithm that combines least mean modulus (LMM) algorithm with simple recurrent calculation of the inverse covariance matrix of the filter input using the Newton's method. The LMM-Newton algorithm achieves significant improvement in the convergence speed of complex-domain adaptive filters with a strongly correlated...
The use of two simple and robust variable step-size approaches in the adaptation process of the Normalized Least Mean Square (NLMS) algorithm (VSS-NLMS) in the adaptive channel equalization is investigated. The NLMS algorithm with a fixed step-size (FSS-NLMS) usually results in a trade-off between the residual error and the convergence speed of the algorithm. It is proved by computer simulation that...
In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algorithm, the convergence behavior of these algorithms is independent of the input data correlation statistics. Moreover, the tracking analysis of these algorithms is carried out in the presence of two sources of nonstationarites (carrier frequency offset between transmitter and receiver, and random variations...
For the multisensor systems with unknown noise variances, using correlation method and least squares fusion criterion, information fusion noise variance estimators are presented by the average of local noise variance estimators, which have the consistence. Substituting the fused noise variance online estimators into the optimal Riccati equation and the optimal weighted measurement fusion Kalman filter,...
In this work, we present a novel adaptive filtering scheme that builds on and advances the method of joint iterative optimization (JIO) of reduced-rank filters proposed in [1]. The scheme applies the theory of error bounded set-membership filtering to both the adaptation of the bank of full-rank filters that form the projection matrix, and the reduced-rank adaptive filter that operates in the lower...
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