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In order to address the problem that the direction‐of‐arrival (DOA) estimation is restricted by the number of array elements, this study comes up with a novel DOA estimation algorithm based on discrete‐Fourier‐transformation (DFT) and multiple regression, which focuses on the direct data domain from each array element rather than the traditional covariance domain of the whole array. In this algorithm, the authors regard each channel of the array signal model as an independent form of multiple regression and achieve the DOA by estimating the regression coefficient of the reconstructed source signal from each array element one by one iteratively. In addition, the DOAs estimated from the multiple array elements are averaged to improve the accuracy by exploiting the asymptotic normality. This algorithm is proposed, modelled, and developed theoretically at first. Then the capabilities that the authors' algorithm can estimate not only the very close DOAs since they distinguish the irrelevant sources by the DFT on temporal frequency but also the DOAs when the number of sources is larger than that of array elements are fully demonstrated by simulations. Furthermore, no requirement for the known number of sources brings the algorithm broader application foreground. [ABSTRACT FROM AUTHOR]

In order to address the problem that the direction‐of‐arrival (DOA) estimation is restricted by the number of array elements, this study comes up with a novel DOA estimation algorithm based on discrete‐Fourier‐transformation (DFT) and multiple regression, which focuses on the direct data domain from each array element rather than the traditional covariance domain of the whole array. In this algorithm, the authors regard each channel of the array signal model as an independent form of multiple regression and achieve the DOA by estimating the regression coefficient of the reconstructed source signal from each array element one by one iteratively. In addition, the DOAs estimated from the multiple array elements are averaged to improve the accuracy by exploiting the asymptotic normality. This algorithm is proposed, modelled, and developed theoretically at first. Then the capabilities that the authors' algorithm can estimate not only the very close DOAs since they distinguish the irrelevant sources by the DFT on temporal frequency but also the DOAs when the number of sources is larger than that of array elements are fully demonstrated by simulations. Furthermore, no requirement for the known number of sources brings the algorithm broader application foreground. [ABSTRACT FROM AUTHOR]

A modified least‐squares algorithm with forgetting factor is proposed and applied to linear time‐varying systems. The bound on the Euclidean norm of the estimation error is derived mathematically. When the bounded regressor of the linear time‐varying system satisfies persistent excitation condition, this new algorithm can attenuate the influences of bounded disturbances and the change ratio of time‐varying parameters effectively through extra term that is added to the covariance's update law. Contrary to the earlier proposed estimation laws, this algorithm does not require any prior knowledge of upper bounds on the parameters and the disturbances. Simulation results demonstrate the effectiveness of the identification law. [ABSTRACT FROM AUTHOR]

An attitude determination covariance measurement model for unit vector sensors with a wide field-of-view is analyzed and compared to the classic QUEST covariance model. The wide field-of-view model has been previously proposed as a more realistic alternative for sensors where measurement accuracy depends on angular distance from the boresight axis. Both QUEST and the wide field-of-view models are evaluated relative to a measurement model that uses the two-dimensional sensor focal plane measurements directly, rather than first converting them to unit vectors. The Fisher information matrix is derived for attitude determination based on such direct sensor measurements, and the wide field-of-view measurement model is shown to have the same Fisher information matrix. Numerical simulations confirm that an extended Kalman filter based on the wide field-of-view model outperforms a filter based on the QUEST measurement model, and also that the wide field-of-view 3 σ bounds are effectively identical to those of a filter based on the direct two-dimensional sensor measurements. [ABSTRACT FROM AUTHOR]

We introduce covariance analysis models for circular dependent variables. In addition, we have also developed hypothesis tests to evaluate the significance of the parameters used. One of the tests is an extension of the Watson-Williams test. The tests performances have been evaluated using simulation studies. [ABSTRACT FROM AUTHOR]

Abstract: We consider one-way analysis of covariance (ANCOVA) model with a single covariate when the distribution of error terms are short-tailed symmetric. The maximum likelihood (ML) estimators of the parameters are intractable. We, therefore, employ a simple method known as modified maximum likelihood (MML) to derive the estimators of the model parameters. The method is based on linearization of the intractable terms in likelihood equations. Incorporating these linearizations in the maximum likelihood, we get the modified likelihood equations. Then the MML estimators which are the solutions of these modified equations are obtained. Computer simulations were performed to investigate the efficiencies of the proposed estimators. The simulation results show that the proposed estimators are remarkably efficient compared with the conventional least squares (LS) estimators. [Copyright &y& Elsevier]

In this paper, covariance behavior of several features (signature identifiers) that are determined from the voltage wave- form within a time window for power-quality (PQ) event detection and classification is analyzed. A feature vector using selected signature identifiers such as local wavelet transform extrema at various decomposition levels, spectral harmonic ratios, and local extrema of higher order statistical parameters, is constructed. It is observed that the feature vectors corresponding to power quality event in stances can be efficiently classified according to the event type using a covariance based classifier known as the common vector classifier. Arcing fault (high impedance fault) type events are successfully classified and distinguished from motor startup events under various load conditions. It is also observed that the proposed approach is even able to discriminate the loading conditions within the same class of events at a success rate of 70%. In addition, the common vector approach provides a redundancy and usefulness information about the feature vector elements. Implication of this information is experimentally justified with the fact that some of the signature identifiers are more important than others for the discrimination of PQ event types. [ABSTRACT FROM AUTHOR]

Air pollution in the cabins of 101 Japanese cars due to organic compounds diffusing from the interior materials has been described in our previous report. In the present study, the influence of the manufacturer, specifications and usage status of these cars on the interior air pollution was evaluated by covariance analysis. Also, the levels of air pollution in the initial phases of delivery as a new car were estimated using previous data for the time-courses of interior concentrations of organic compounds measured in another new car. The findings showed greater air pollution in the cabins of luxury cars, with leather seats or leather steering wheels, or high-end catalogue prices. Differences in the specifications contributed more markedly to interior air pollution than differences in manufacturers. Also, usage status, such as everyday ventilation affected the long-term interior air quality. The sum values of interior concentrations of 154 compounds, for which there were time-course data, were estimated to be approximately 1700μg·m-3 as a median (max. 11,000μg·m-3) at 1 month from delivery (interior temperature, 32 °C; interior humidity, 45%). [ABSTRACT FROM AUTHOR]