Presence Of Unknown Noise Research Articles

A sliding mode control for wave energy converters in presence of unknown noise and nonlinearities

A relatively simple, optimal, and robust nonlinear Sliding Mode Control (SMC) is developed in this paper for a novel nonlinear Wave Energy Converter (WEC). Giving that deriving an optimal control for a complex system is very cumbersome or nearly impossible, an SMC (purely switching only) is thus implemented with the sliding surface defined in an optimal sense. Despite it is simple, the optimality of this control is proved by using a new equivalent control analysis mathematically and also demonstrated numerically. In addition, the robustness of the control is shown mathematically with Lyapunov analysis and by simulations with uncertainties. The main contributions of this paper are: (1) a novel Sliding Mode Control is developed which is simple, optimal, and robust;(2) the robustness and optimality of the proposed control are both proved mathematically and verified numerically; (3) a weakly nonlinear model is developed for a nonlinear WEC which is utilized to validate the performance of the proposed nonlinear control. The numerical simulations are then conducted on a weakly nonlinear model developed for the nonlinear WEC. The results show the robustness and optimality of the proposed control (compared with a nonlinear damping control) subject to uncertainties and nonlinearities.

Enhanced Performance of Multi Class Classification of Anonymous Noisy Images

An important constituents for image classification is the identification of significant characterstics about the specific class to distinguish intra class variations. Since performance of the classifiers is affected in the presence of noise, so selection of discriminative features is an important phase in classification. This superfluous information i.e. noise, e.g. additive noise may occur in images due to image sensors i.e. of the constant noise level in dark areas of the image or salt & pepper noise may be caused by analog to digitals conversion and bit error transmission etc.. Detection of noise is also very essential in the images for choosing appropriate filter. This paper presents an experimental assessment of neural classifier in terms of classification accuracy under three different constraints of images without noise, in presence of unknown noise and after elimination of noise.

Blind Identification of FIR Channels in the Presence of Unknown Noise

AbstractBlind channel identification techniques based on second-order statistics (SOS) of the received data have been a topic of active research in recent years. Among the most popular is the subspace method (SS) proposed by Moulines et al. (1995). It has good performance when the channel output is corrupted by white noise. However, when the channel noise is correlated and unknown as is often encountered in practice, the performance of the SS method degrades severely. In this paper, we address the problem of estimating FIR channels in the presence of arbitrarily correlated noise whose covariance matrix is unknown. We propose several algorithms according to the different available system resources: (1) when only one receiving antenna is available, by upsampling the output, we develop the maximum a posteriori (MAP) algorithm for which a simple criterion is obtained and an efficient implementation algorithm is developed; (2) when two receiving antennae are available, by upsampling both the outputs and utilizing canonical correlation decomposition (CCD) to obtain the subspaces, we present two algorithms (CCD-SS and CCD-ML) to blindly estimate the channels. Our algorithms perform well in unknown noise environment and outperform existing methods proposed for similar scenarios.

Adaptive Detection of a Signal Known Only to Lie on a Line in a Known Subspace, When Primary and Secondary Data are Partially Homogeneous

This paper deals with the problem of detecting a signal, known only to lie on a line in a subspace, in the presence of unknown noise, using multiple snapshots in the primary data. To account for uncertainties about a signal's signature, we assume that the steering vector belongs to a known linear subspace. Furthermore, we consider the partially homogeneous case, for which the covariance matrix of the primary and the secondary data have the same structure but possibly different levels. This provides an extension to the framework considered by Bose and Steinhardt. The natural invariances of the detection problem are studied, which leads to the derivation of the maximal invariant. Then, a detector is proposed that proceeds in two steps. First, assuming that the noise covariance matrix is known, the generalized-likelihood ratio test (GLRT) is formulated. Then, the noise covariance matrix is replaced by its sample estimate based on the secondary data to yield the final detector. The latter is compared with a similar detector that assumes the steering vector to be known

Maximum likelihood DOA estimation and asymptotic Cramer-Rao bounds for additive unknown colored noise

This paper is devoted to the maximum likelihood estimation of multiple sources in the presence of unknown noise. With the spatial noise covariance modeled as a function of certain unknown parameters, e.g., an autoregressive (AR) model, a direct and systematic way is developed to find the exact maximum likelihood (ML) estimates of all parameters associated with the direction finding problem, including the direction-of-arrival (DOA) angles /spl Theta/, the noise parameters /spl alpha/, the signal covariance /spl Phi//sub s/, and the noise power /spl sigma//sup 2/. We show that the estimates of the linear part of the parameter set /spl Phi//sub s/ and /spl sigma//sup 2/ can be separated from the nonlinear parts /spl Theta/ and /spl alpha/. Thus, the estimates of /spl Phi//sub s/ and /spl sigma//sup 2/ become explicit functions of /spl Theta/ and /spl alpha/. This results in a significant reduction in the dimensionality of the nonlinear optimization problem. Asymptotic analysis is performed on the estimates of /spl Theta/ and /spl alpha/, and compact formulas are obtained for the Cramer-Rao bounds (CRB's). Finally, a Newton-type algorithm is designed to solve the nonlinear optimization problem, and simulations show that the asymptotic CRB agrees well with the results from Monte Carlo trials, even for small numbers of snapshots. >