In the ensemble Kalman filter (EnKF), uncertainty in the state of a dynamical model is represented as samples of the state vector. The samples are propagated forward using the evolution model, and the forecast (prior) mean and covariance matrix are estimated from the ensemble. Data assimilation is carried out by using these estimates in the Kalman filter formulas. The prior is given in the subs...

OBJECTIVE Given conflicting recommendations in the literature, this report seeks to present a standard protocol for applying principal components analysis (PCA) to event-related potential (ERP) datasets. METHODS The effects of a covariance versus a correlation matrix, Kaiser normalization vs. covariance loadings, truncated versus unrestricted solutions, and Varimax versus Promax rotations wer...

The full covariance solution to simultaneous localization and map building based on extended Kalman filter requires update time quadratic in the number of landmarks in the map. In order to improve the computational efficiency, this paper reorganizes system state vector and system models. The state of mobile robot is redefined and represented indirectly. The higher dimensional system models and ...

The lack of the latest measurement information and the Particle serious degradation cause low estimation precision in the tradition particle filter SLAM (simultaneous localization and mapping). For solve this problem, a SRCPF-SLAM (square cubature particle filter simultaneous localization and mapping) is proposed in this paper. The algorithm fuses the latest measurement information in the stage...

High resolution eigenstructure-based techniques for signal source localization are known to be ineffective when the source covariance matrix is not of full rank. We present here two techniques to circumvent this problem in the context of wideband active source localization. An extension is made to show how eigenstructure methods can be applied even when there is only one snapshot available to e...

OBJECTIVE Electroencephalography (EEG) and magnetoencephalography (MEG) non-invasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to this problem have...

In this paper, the methods and analysis for estimating the location of a three-dimensional (3-D) single source buried in lossy medium are presented with uniform circular array (UCA). The mathematical model of the signal in the lossy medium is proposed. Using information in the covariance matrix obtained by the sensors' outputs, equations of the source location (azimuth angle, elevation angle, a...

In this study, we introduce an efficient pre-processing scheme for direction of arrival (DOA) estimation, which is capable of reducing the noise and reverberation effects in speech sound source localization. Furthermore, this presented system is also suitable for far-field speech localization. The adopted method of this proposed system can be simply subdivided into three stages: Linear phase-di...

We characterize the accuracy of a cooperative localization algorithm based on Kalman Filtering, as expressed by the trace of the covariance matrix, in terms of the algebraic graph theoretic properties of the sensing graph. In particular, we discover a weighted Laplacian in the expression that yields the constant, steady state value of the covariance matrix. We show how one can reduce the locali...