In this paper, we consider the schedule-based network localization concept, which does not require synchronization among nodes and does not involve communication overhead. The concept makes use of a common transmission sequence, which enables each node to perform self-localization and to localize the entire network, based on noisy propagation-time measurements. We formulate the schedule-based l...

As space-object catalogs move from being maintained by general perturbations to special perturbations, and as the number of detectable objects in space increase, sequential (recurrent) estimators have some significant advantages over batch processing that should not be overlooked. A primary advantage of a sequential estimator is that the time evolution of the satellite state error covariance ca...

Multicollinearity is typically thought of as a problem of large standard errors resulting from the near linear dependence of independent variables. One solution is to have more informative data, possibly in the form of a larger sample. In this paper I argue that this understanding of multicollinearity is only partially correct. The near collinearity of independent variables results in regressio...

In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo methods for large–scale applications with high dimensional parameter spaces, e.g. in uncertainty quantification in porous media flow. We propose a new multilevel Metropolis-Hastings algorithm, and give an abstract, problem dependent theorem on the cost of the new multilevel es...

Trilateration techniques use distance measurements to survey the spatial coordinates of unknown positions In practice distances are measured with error and statistical methods can quantify the uncertainty in the estimate of the unknown location Three methods for estimating the three dimensional position of a point via trilateration are presented a linear least squares estimator an iteratively r...

In this paper we present two nonlinear estimation algorithms that combine generalized polynomial chaos theory with higher moment updates and Bayesian framework. Polynomial chaos theory is used to predict the evolution of uncertainty of the nonlinear random process. In the first estimation algorithm, higher order moment updates are used to estimate the posterior non Gaussian probability density ...

Systems containing uncertainty are traditionally analyzed with probabilistic methods. However, for non-linear, non-Gaussian systems solutions can sometimes be very di4cult to obtain. The focus of this work is to determine if in such cases fuzzy dynamic system models may provide an alternative approach that more easily leads us to a good solution. In this paper, we present a fuzzy estimator whos...

Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it. In this paper we compare three techniques for computing conditional density estimates using a class probability estimator, where this estimator is ap...

Hall’s recent derivation of an exact uncertainty relation [Phys. Rev. A64, 052103 (2001)] is revisited. It is found that the Bayes estimator of an observable between preand postselection equals the real part of the weak value. The quadratic loss function equals the expectation of the squared imaginary part of the weak value.

This paper addresses a problem of set-valued state estimation for uncertain continuous-time systems via limited capacity communication channels. The uncertainty of the systems satisfies an integral quadratic constraint. Using results from the robust Kalman filtering, we design a coder/decoder-estimator pair that allows us to construct set-valued state estimate of the systems via communication c...