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A technique is presented for estimating the covariance matrix due to underwater ambient noise by enforcing frequency- and angle-smoothness on the power-angle spectrum underlying an observed sample covariance matrix. This smoothing is achieved through a combination of least-squares fitting procedures and explicit suppression of contributions from discrete broadband emitters. The process preserves contributions...
This paper presents an new approach to noise reduction for voice communication over Bluetooth technology. In the literature, several authors have compared the performance of different filtering techniques, such as the well-known Spectral Subtraction (SS), and Wiener Filter (WF) using simulated data, whereas this research uses real-time data samples collected from cars subjected to a noisy environment...
This study explores the non-parametric estimation of a shape boundary from noisy points in 2D when the sensor characteristics are known. As the underlying shape information is not known, the offered algorithm estimates points on the shape boundary by using the statistics of the subsets of point cloud data. The novel approach proposed in this paper is able to find corner points in a local geometry...
INS and GNSS integrated systems have become widespread as a result of low-cost MEMS inertial sensor technology. However, the accuracy of computed velocity and orientation is not sufficient for some applications, e.g. performance and technique monitoring and evaluation in sports. Significant accuracy improvements can be made by post-mission data processing. The approach is based on fixed-lag Rauch-Tung-Striebel...
Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop...
This paper is concerned with the problem of optimal state estimation for multi-delay wireless network systems with nonzero-mean processes and measurement deviation under multiple packet dropouts. The orthogonal projection principle and reorganized innovation analysis approach are used to address the proposed problem. m + 1 Riccati equations of same dimension are given to solve the Kalman filter parameters...
This paper formulates and studies the problem of distributed filtering based on randomized gossip strategy in order to estimate the state of a dynamic system via all sensors in a network. First we introduce the randomized gossip algorithm by which the fastest averaging strategy can be obtained for a network with an arbitrary topology. Then we combine the randomized gossip algorithm with the information...
In this paper, we present an extended Tobit Kalman filter that deals with fault detection problem in nonlinear systems with missing measurements and censored data. The missing measurements randomly occurring are regulated by individual random variables whose probability distributions are on the interval [0,1]. The censored data are characterized by the Tobit measurement model. The Tobit Kalman filter...
Gyro-less attitude and angular rate estimations are of great importance in small, low-cost spacecraft, where high performance gyroscopes are not available due to multiple limitations. Recent development of accurate, high-bandwidth attitude sensors such as high data-rate star trackers, makes this approach implementable. The gyro-less estimator propagates the estimated states by nonlinear attitude dynamics...
New sensorless virtual-flux (VF) based predictive direct power control (PDPC) (VF-PDPC) of a three-phase pulse-width modulation (PWM) rectifier is developed in this paper. The VF estimation is achieved through a simple neural filter based integrator (NF-I) in series with a multi-output adaptive linear neuron (MO-ADALINE). The NF-I leads to cancel dc offset and harmonic distortions in the estimated...
The paper utilizes a novel battery model based on the electrical features of LiFePO4 battery, because Kalman filter algorithm(KF) is largely dependent on system model. Measurements of battery state are easily disturbed by colored noise which is high relevance in working condition, and the paper studies that the system noise satisfy one-order AR model. The paper proposes an adaptive extended Kalman...
In this paper, we propose a new time-frequency mask method for computational auditory scene analysis (CASA) based on convex optimization of the binary mask. In the proposed method, the pitch estimation and segment segregation in conventional CASA are completely replaced by the convex optimization of speech power. Considering the cross-correlation between the power spectra of noisy speech and noise...
This paper presents the design of Unscented Kalman Filter (UKF) for estimation of state space variables of permanent magnet synchronous machine (PMSM). The UKF is shown together with the field oriented speed control. At first, the position and the speed of PMSM are measured, and UKF is used only for a load torque estimation. It is indicated how differences in sampling time of the speed and the current...
The performance of the three-axis magnetometer is mainly restricted by the sensitivity error, offset error, and nonorthogonality error. In this paper, we present a geometric ellipsoid fitting method to calibrate these errors. The new method is based on minimizing the orthogonal distances from measuring points to the ellipsoid. The minimizing problem is formulated as the nonlinear least square problem,...
Electroencephalographic (EEG) recordings are widely used in the analysis of the brain signals, the EEG data are recorded in combination with background activities like noise, and with artifacts of physiological or technical origins. Independent component analysis (ICA) is a method that allows blind separation of sources. This technique includes FastICA, which is used here to identify artifacts from...
In this paper, we present a novel image reconstruction algorithm for positron emission tomography(PET). Almost all of existing reconstruction approaches assume that the measurement model for PET is linear equation with Gaussian white noise or energy-bounded noise, which only approximates the emission and detection of PET very roughly. In fact, the real situation is much more complicated than the one...
Label estimation is an important component in an unsupervised person re-identification (re-ID) system. This paper focuses on cross-camera label estimation, which can be subsequently used in feature learning to learn robust re-ID models. Specifically, we propose to construct a graph for samples in each camera, and then graph matching scheme is introduced for cross-camera labeling association. While...
This paper proposes a novel algorithm for the joint estimation of DOA, range and frequency of mixed far-field and near-field sources. Based on the second order statistics of a symmetric uniform linear array, this method constructs two correlation matrices in the first step to obtain the estimates of DOA and frequency parameters. In the second step, two more correlation matrices are derived. The range...
One of the challenging aspects in directional electromagnetic measurements is to accurately estimate the formation bedding orientation angle, which is critical to help maintain drilling the wellbore in the sweetest spots of the reservoir. Various existing methods can be both noisy and susceptible to phase wrapping issues. Here we present a new approach for accurately computing the bedding orientation...
Current study performs a comparison of the contour inflection point selection methods according to the following criteria: probability of the correct selection, probability of the incorrect selection and an error in the coordinate estimation based on the results of model tasks. Results of the interpolation and differential methods and the method based on wavelet analysis of the contour inflection...
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