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In this paper, we consider the spectrum sensing problem of detecting a primary signal of a macro cell in a cognitive radio network by employing multiple dense small cell base stations. In consideration of the number of cooperative small cells (sample dimension) is comparable to the number of sample (sample size) due to the dense deployment of small cells, sample covariance matrix is no more a good...
In the last decade, monocular simultaneous localization and mapping (mono-SLAM) has appeared as another alternative for pose estimation, but this last gives a localization up to scale, and suffers from scale drift due to the difficulty of depth evaluation; however, several approaches had been tackled to recover the scale and take off the ambiguity. Both methods were designed to get the accurate scale...
This paper aims at presenting a solution to the Simultaneous Localization and Mapping (SLAM) problem of Unmanned Ground Vehicles (UGV) by combining information given by an odometer and a laser range finder. The most popular solutions to the SLAM problem are EKF-SLAM and the FAST-SLAM algorithms. The first one solution, have some important limitations which have need of an accurate process and an observation...
This paper addresses the image deblurring problem, where a known linear space-invariant point-spread function (PSF) is to be deconvoluted from a given blurry image, with addictive zero-mean white and homogeneous Gaussian additive noise. We propose a novel MAP image deblurring method based on sparse representation and Laplacian mixture modeling. The research contents mainly include: (1) Establish the...
In this paper, the relevance of deep neural network (DNN) is studied in big data scenarios, specifically for prognostics applications. It is observed that fault predictions can be performed more efficiently when DNN is used with a pre-processing step. A novel hierarchical dimension reduction (HDR) approach is therefore proposed as a pre-processing step to DNN. This two-step approach is shown to be...
This study applies the unscented Kalman filter (UKF) and the ensemble Kalman filter (EnKF) to estimate a contraction ratio of the McKibben pneumatic artificial muscle (PAM) and to present that the UKF is more effective than the EnKF in estimating the PAM length. Both filters were applied for a commercial PAM, FESTO DMSP-20-200N, to validate them. The estimation was conducted offline under three types...
We consider the estimation of the state transition matrix in vector autoregressive models when the time sequence data is limited but nonsequence steady-state data is abundant. To leverage both sources of data, we formulate the problem as the least-squares minimization regularized by a Lyapunov penalty. Explicit cardinality or rank constraints are imposed to reduce the complexity of the model. The...
Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance,...
Motor imagery BCI is a system that is very useful to help people with disabilities who can't move their limbs. These systems use brain activity patterns that are made from motor imagery without actual movement. In this paper, we proposed enhanced One Versus One (OVO) structure to classify EEG-based multi-class motor imagery signals. Also, shrinkage estimator based Common Spatial Pattern (CSP) is used...
In this work, well known Sigma Point Kalman Filters (SPKFs); namely Unscented Kalman filter (UKF), the Cubature Kalman filter (CKF), and the Central Differences Kalman filter (CDKF) will be combined to the Smooth Variable Structure Filter (SVSF), in order to create stable and robust algorithms that can be applied to highly non-linear systems. The proposed algorithms will be applied into 4-DOF robotic...
Recently unmanned aerial vehicles have become one of the most interesting research topics among scientists. Although researchers are very concerned in this area, they generally use the same dynamical plant equations. Simulations using that ready-to-use plant equations are common methods in order to apply control or optimization theories. But, beyond simulations, in real time control of these systems...
We have studied Chan-Taylor two-dimensional positioning algorithm and propose an innovative Chan-Taylor three-dimensional positioning algorithm. And we apply it to the indoor three-dimensional location problem based on wireless communication base station. According to the actual measurement of the coordinates of the 30 base stations and the value of time of arrival (TOA) between the 1100 mobile phone...
The purpose of this paper is to derive new asymptotic properties of the robust adaptive normalized matched filter (ANMF). More precisely, the ANMF built with Tyler estimator (TyE-ANMF) is analyzed under the framework of complex elliptically symmetric (CES) distributions. We show that the distribution of TyE-ANMF can be accurately approximated by the well-known distribution of the ANMF built with the...
Classic approaches to multi-channel signal enhancement rely on model assumptions regarding speech source relative transfer functions and noise covariance matrix, or on estimates thereof obtained in, e.g., speech pauses. To alleviate these constraints, we here investigate an approach to adaptive estimation of the speech (target) source and noise related acoustic parameters based on localized speech...
We consider the problem of estimating the covariance matrix and the transition matrix of vector autoregressive (VAR) processes from partial measurements. This model encompasses settings where there are limitations in the data acquisition of the underlying measurement systems so that data is lost or corrupted by noise. An estimator for the covariance matrix of the observations is first presented. More...
Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local meansurements/observations are scattered over a wide geographical area. However, the convergence of Gaussian BP is still an open issue. In this paper, we consider the convergence of Gaussian BP, focusing in particular...
We consider the problem of learning dictionaries for data compression. Different from ordinary learning methods, the objective is to design a dictionary such that the signal has a low entropy representation in the basis of the dictionary, rather than giving a sparse or low-energy representation. To achieve this goal, we need to consider the effect of quantization on the rate-distortion curve as well...
In this paper we have proposed clock error mitigation from the measurements in the scheduled based self localization system. We propose measurement model with clock errors while following a scheduled transmission among anchor nodes. Further, RLS algorithm is proposed to estimate clock error and to calibrate measurements of self localizing node against relative clock errors of anchor nodes. A full-scale...
Regularized Tyler Estimator's (RTE) have raised attention over the past years due to their attractive performance over a wide range of noise distributions and their natural robustness to outliers. Developing adaptive methods for the selection of the regularisation parameter α is currently an active topic of research. Indeed, the bias-performance compromise of RTEs highly depends on the considered...
In this paper, we consider robust direction-of-arrival (DOA) estimation for an array that contains mis-calibrated sensors with unknown gain and phase uncertainties. We develop two robust DOA estimation algorithms based on the maximum correntropy criterion (MCC). In the first algorithm, adaptively optimized weighting factors are obtained and applied to each sensor to effectively mitigate the effect...
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