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Linear estimators have been extensively utilized for wireless location estimation for their simplicity and closed form property. In the paper, the class of linear estimator by introducing an additional variable, e.g., the well-adopted linear least squares (LLS) estimator, is discussed. There exists information loss from the linearization of location estimator to the nonlinear location estimation, which prevents the linear estimator from approaching the Cramér-Rao lower bound (CRLB). The linearized location estimation problem-based CRLB (L-CRLB) is derived in this paper to provide a portrayal that can fully characterize the behavior for this type of linearized location estimator. The relationships between the proposed L-CRLB and the conventional CRLB are obtained and theoretically proven in this paper. As suggested by the L-CRLB, higher estimation accuracy can be achieved if the mobile station (MS) is located inside the convex hull of the base stations (BSs) compared to the case that the MS is situated outside of the geometric layout. This result motivates the proposal of geometry-assisted localization (GAL) algorithm in order to consider the geometric effect associated with the linearization loss. Based on the initial estimation, the GAL algorithm fictitiously moves the BSs based on the L-CRLB criteria. Two different implementations, including the GAL with two-step least squares estimator (GAL-TSLS) and the GAL with Kalman filter (GAL-KF), are proposed to consider the situations with and without the adoption of MS's historical estimation. Simulation results show that the GAL-KF scheme can compensate the linearization loss and improve the performance of conventional location estimators. [ABSTRACT FROM AUTHOR]

In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of the simultaneous estimation of frequency and power components is investigated. It is solved through a new recursive estimator capable of estimating the unknown model parameters during severe dynamic changes in the system. By this, a simple nonlinear parameter model for the instantaneous power, taking into account the fundamental components of voltage and current, is used as a starting point for the power components and frequency estimation. In the model, the system frequency is considered as an unknown model parameter, and it is simultaneously estimated with other unknown parameters. This resulted in an efficient numerical algorithm for the measurement of power components, which is not sensitive to variations of the system frequency. This is an important achievement, which improves the accuracy of the method during network transients. The new estimator has been improved by the strategy for tuning its forgetting factor according to the system dynamics, and it is tested through computer simulations and by using data records obtained under laboratory conditions. The algorithm was developed for a single-phase system, and the approach might be easily extended to multiphase systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel iterative calibration algorithm for triaxial magnetometers. The proposed algorithm estimates and compensates the effects of deterministic interference parameters using only nine distinct measurements. The results of our extensive simulations and empirical evaluations confirm that the proposed method outperforms conventional ellipsoid fitting based models both in terms of accuracy and reliability even in the presence of a moderate wideband noise. The algorithm also achieves considerably faster convergence, which makes it suitable for real-time applications. The algorithm performance is also shown to be independent of the initial guesses of the interference parameters. [ABSTRACT FROM PUBLISHER]

Super-resolution is a widely applied technique that improves the resolution of input images by software methods. Most conventional reconstruction-based super-resolution algorithms assume accurate dense optical flow fields between the input frames, and their performance degrades rapidly when the motion estimation result is not accurate enough. However, optical flow estimation is usually difficult, particularly when complicated motion is presented in real-world videos. In this paper, we explore a new way to solve this problem by using sparse feature point correspondences between the input images. The feature point correspondences, which are obtained by matching a set of feature points, are usually precise and much more robust than dense optical flow fields. This is because the feature points represent well-selected significant locations in the image, and performing matching on the feature point set is usually very accurate. In order to utilize the sparse correspondences in conventional super-resolution, we extract an adaptive support region with a reliable local flow field from each corresponding feature point pair. The normalized prior is also proposed to increase the visual consistency of the reconstructed result. Extensive experiments on real data were carried out, and results show that the proposed algorithm produces high-resolution images with better quality, particularly in the presence of large-scale or complicated motion fields. [ABSTRACT FROM AUTHOR]

This paper outlines a more powerful formulation of a previously published new concept in blind seismic deconvolution, referred to as principle phase decomposition (PPD). In this new PPD filter formulation, an iterative forward modeling (IFM) algorithm is incorporated, which facilitates the estimation of parameters defining the source wave (i.e., dominant frequency, phase, and decay) and the overlapping source waves (i.e., reflection coefficients' corresponding arrival times and amplitudes). This IFM integrated PPD algorithm allows for a significantly more accurate approach in estimating the source wave and corresponding reflection series compared to the previously published technique of sequentially estimating the source wave and overlapping source waves utilizing a Rao–Blackwellized particle filter. In general terms, the source wave is modeled as an amplitude-modulated sinusoid, and the overlapping source waves are treated as known inputs within the Kalman filter formulation based on the current source wave and reflection series IFM parameter estimates. The source wave and reflection series parameters are obtained by iteratively minimizing a cost function defined to be the rms difference between the measured seismogram and the synthesized seismogram within the IFM algorithm. [ABSTRACT FROM AUTHOR]

We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from consideration. We propose a bias-elimination technique and develop a novel class of diffusion LMS algorithms that can mitigate the effect of regression noise and obtain an unbiased estimate of the unknown parameter vector over the network. In our development, we first assume that the variances of the regression noises are known a priori. Later, we relax this assumption by estimating these variances in real time. We analyze the stability and convergence of the proposed algorithms and derive closed-form expressions to characterize their mean-square error performance in transient and steady-state regimes. We further provide computer experiment results that illustrate the efficiency of the proposed algorithms and support the analytical findings. [ABSTRACT FROM AUTHOR]

A parametric estimation algorithm for a single-axis gradiometer is described. The most influential parameters are isolated and estimated; their bias and variance are discussed. Even though the system is closed-loop controlled, only plant parameters are estimated using an open-loop identification equation, which simplifies the identification procedure. The estimates are obtained using the method of instrumental variables (IV). The advantage of this method is that it remains bias-free even when the system states are correlated with the measurement noise. The problem is described in a one-dimensional frame, but it can be extended to the multivariate case if necessary. The estimated parameters are then used to reconstitute the closed-loop transfer function, which allows the input disturbance that caused the observed signal to be deduced. The estimation algorithm is applied to the laser interferometer space antenna (LISA) technology package, which is the scientific payload of the LISA Pathfinder spacecraft (SC), an ESA/NASA technology demonstrator to be launched in 2014. The analysis is operated on a simulated dataset produced with the mission end-to-end simulator (E2E) as well as with a simple linear simulator. The on-board software and hardware constraints are taken into account since they are important performance drivers. The results are detailed along with the experiment model. [ABSTRACT FROM PUBLISHER]

We study the use of the FitzHugh–Nagumo (FHN) model for capturing neural spiking. The FHN model is a widely used approximation of the Hodgkin–Huxley model that has significant limitations. In particular, it cannot produce the key spiking behavior of bursting. We illustrate that by allowing time-varying parameters for the FHN model, these limitations can be overcome while retaining its low-order complexity. This extension has applications in modeling neural spiking behaviors in the thalamus and the respiratory center. We demonstrate the use of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits its multiple time-scale properties, and compare the performance of this method with the extended Kalman filter in several illustrative examples. We demonstrate that the dynamics of the spiking threshold can be recovered even in the absence of complete specifications for the system. [ABSTRACT FROM PUBLISHER]

Considering a real signal as the sum of a number of sinusoidal signals in the presence of additive noise, maximum windowed likelihood (MWL) criterion is introduced and applied to construct an adaptive algorithm in order to estimate the amplitude and frequency of these components. The amplitudes, phases and frequencies are assumed to be slowly time varying. Employing MWL an adaptive algorithm is obtained in two steps. First, assuming some initial values for the frequency of each component, a closed form is derived to estimate the amplitudes. Then, the gradient of MWL is used to adaptively track the frequencies, using the latter values of amplitudes. The proposed algorithm has a parallel structure in which each branch estimates parameters of one of the components. The proposed multicomponent phase locked loop (MPLL) algorithm is implemented employing low complexity blocks. It is adjustable to be used in different conditions. The mean squared error of the algorithm is studied to analyze the effect of the window length and type and the step size. Simulations have been conducted to illustrate the efficiency and the performance of the algorithm in different conditions including: the effect of the initialization, the frequency resolution, for chirp components, for components during frequency crossover and for speech signals. Simulations illustrate that the method efficiently tracks slowly time-varying components of the signals such as voiced speech segments. [ABSTRACT FROM AUTHOR]