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ABSTRACT: Distributed wireless sensor networks have been proposed as a solution to environment sensing, target tracking, data collection and others. Energy efficiency, high estimation accuracy, and fast convergence are important goals in distributed estimation algorithms for WSN. This paper studies the problem of robust adaptive estimation in impulsive noise environment using robust cost function like Wilcox on norm and error saturation nonlinearity. The incremental cooperative scheme conventionally used in sensor network in which each node have local computing ability and share them with their predefined neighbors, is not robust to impulsive type of noise or outliers. In this paper the robust norm is introduced in incremental cooperative distributed network to estimate the desired parameters in presence of Gaussian contaminated impulsive noise.
Computational Intelligence and Communication Networks (CICN), 2010 International Conference on; 12/2010
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ABSTRACT: It is known that sign sign LMS and sign regressor LMS are faster than LMS. Inspiring from this idea we have proposed sign regressor Wilcoxon and sign-sign wilcoxon which are robust against the outlier present in the desired data and also faster than Wilcoxon and sign Wilcoxon norm. It had applied to varities of linear and nonlinear system identification problems with Gaussian noise and impulse noise present in the desired. The simulation results are compared among Wilcoxon,sign Wilcoxon and proposed sign-sign Wilcoxon and sign-regressor Wilcoxon. From simulation results it has proved that the proposed techniques are robust against outlier in the desired data and convergence speed are faster compared to other two norms.
Advances in Recent Technologies in Communication and Computing (ARTCom), 2010 International Conference on; 11/2010
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ABSTRACT: The main challenges in estimating parameters in a sensor network are link failure and impulsive noise. Under such adverse conditions the conventional square error cost function based learning algorithms offer poor estimation performance. In this paper we propose a robust error saturation nonlinearity as cost function and use it for decentralized incremental estimation of parameters. Simulation results show that the proposed estimation method offers more robust distributed estimation compared to Huber loss function based estimation both in link failure and impulsive noise conditions.
Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on; 11/2009
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ABSTRACT: This paper introduces two new distributed learning algorithms : Incremental Particle Swarm Optimization (IPSO) and Diffusion Particle Swarm Optimization (DPSO). These algorithms are applied for distributed identification of nonlinear processes using cooperation among adaptive nodes. Identification of four standard nonlinear plants have been carried out through simulation to assess the performance of these algorithms. The results indicate better or identical identification performance offered by the proposed distributed algorithms compared to that offered by the conventional PSO based algorithm. The improvement is observed in terms of CPU time, accuracy in response matching and speed of convergence.
Evolutionary Computation, 2009. CEC '09. IEEE Congress on; 06/2009
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ABSTRACT: A new robust Wilcoxon least mean square (WLMS) algorithm using the Wilcoxon norm as the cost function is developed. Simulation results on identification of all-zero plants using WLMS with outliers present in the training signal clearly demonstrate superior performance over the conventional LMS-algorithm-based approach.
Electronics Letters 04/2009; · 0.96 Impact Factor
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ABSTRACT: The development of an adaptive infinite impulse response (IIR) linear equalizer is described. Using discrete time Wiener filtering theory, a closed form for the optimum mean-square error IIR filter is derived. A performance comparison using both minimum and non-minimum phase channels indicates the complexity/performance advantages inherent in the IIR system compared to an optimum finite impulse response (FIR) solution. The minimum phase spectral factorization, which is an integral part of the derivation of the IIR equalizer, may be circumvented through the use of a Kalman equalizer such as that originally proposed by Lawrence and Kaufman. The structure is made adaptive by using a system identification algorithm operating in parallel with a Kalman equalizer. In common with Luvison and Pirani, a least mean squares (LMS) algorithm was chosen for the system identification because the input to the channel is white and hence the LMS algorithm will produce consistent predictable results with little added complexity. A new technique is introduced which both estimates the variance of channel noise and compensates the Kalman filter for errors in the estimate of the channel impulse response. Computer simulation results show that the convergence performance of this new adaptive IIR filter is roughly equivalent to an FIR equalizer which is trained using a recursive least squares algorithm. However, the order of the new filter is always lower than the FIR filter.
IEEE Transactions on Acoustics Speech and Signal Processing 01/1988;
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ABSTRACT: This paper deals with the development of a unique self-orthogonalizing block adaptive filter (SOBAF) algorithm that yields efficient finite impulse response (FIR) adaptive filter structures. Computationally, the SOBAF is shown to be superior to the least mean squares (LMS) algorithm. The consistent convergence performance which it provides lies between that of the LMS and the recursive least squares (RLS) algorithm, but, unlike the LMS, is virtually independent of input statistics. The block nature of the SOBAF exploits the use of efficient circular convolution algorithms such as the FFT, the rectangular transform (RT), the Fermat number transform (FNT), and the fast polynomial transform (FPT). In performance, the SOBAF achieves the mean squared error (MSE) convergence of a self-orthogonalizing structure, that is, the adaptive filter converges under any input conditions, at the same rate as an LMS algorithm would under white input conditions. Furthermore, the selection of the step size for the SOBAF is straightforward as the range and the optimum value of the step size are independent of the input statistics.
IEEE Transactions on Acoustics Speech and Signal Processing 01/1987;
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ABSTRACT: Using discrete time Wiener filtering theory a closed form for the optimum mean-square error (MSE) infinite impulse response (IIR) linear equaliser is derived. The minimum phase spectral factorisation, which is an integral part of the derivation of the IIR equaliser, may be circumvented through the use of a Kalman equaliser such as that originally proposed by Lawrence and Kaufman. The structure is made adaptive by using a system identification algorithm operating in parallel with a Kalman equaliser. In common with Luvison & Pirani, a least mean squares (LMS) algorithm was chosen for the system identification because the input to the channel is white. A new technique is introduced which both estimates the variance of channel noise and compensates the Kalman filter for errors in the estimate of the channel impulse response.
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '86.; 05/1986
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ABSTRACT: Wireless sensor networks have been proposed as a solution to environment sensing, target tracking, data collection and other applications. Source localization is one of the important problem in wireless sensor network. In literature a decentralized approach using strong antena arrays at each node or sensor arrays at different positions are used to localize the sources. In this paper a purely co-operative method where every node will participate in estimation. The network does the bearing esti-mation by optimizing maximum likelihood function by forming random array among all the nodes. Particle swarm optimization is used to optimize ML function because it is more efficient compared to other evolutionary algorithm like GA. Finally the results are compared with most analyzed MUSIC algorithm.
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ABSTRACT: The paper introduces a novel method of robust identification of complex plants and prediction of bench mark time series. It is assumed that training samples used contain strong out-liers and the cost function chosen in the proposed model is a robust norm called Wilcoxon norm. The weights of the mod-els are updated using population based PSO technique which progressively reduces the robust norm. To demon-strate the robust performance of the proposed technique standard identification and prediction problems are simu-lated and the results are compared with those obtained by conventional MSE norm based minimization method. A sig-nificant improvement in performance is observed in all cases.
