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In this thesis, we focus on Impulse Radio (IR) Ultra-WideBand (UWB) ranging and positioning techniques under indoor propagation environments. IR-UWB, a new carrierless communication scheme using impulses, is a candidate technology for future communication, ...
The framework of graphical models is a cornerstone of applied Statistics, allowing for an intuitive graphical specification of the main features of a model, and providing a basis for general Bayesian inference computations though belief propagation (BP). I ...
Abstract Smartphones collect a wealth of information about their users' environment and activities. This includes GPS (global positioning system) tracks and the MAC (media access control) addresses of devices around the user, and it can go as far as taking ...
A navigation process is to start from a known (initial) position and to ensure a continued localisation of the user during the movement. Consider a pedestrian navigation system which contains a GPS receiver and a set of inertial sensors connected with the ...
The principal concept of navigation is to start from a known (initial) position and to ensure a continued and reliable localisation of the user during his/her movement. The initial position of the trajectory is usually obtained via GPS or defined by the us ...
Milestones in sparse signal reconstruction and compressive sensing can be understood in a probabilistic Bayesian context, fusing underdetermined measurements with knowledge about low level signal properties in the posterior distribution, which is maximized ...
Institute of Electrical and Electronics Engineers2010
We consider the problem of positioning estimation with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths and additive Gaussian noise environments. Most popular positioning algorithms first estimate certain parameters (such as time of arr ...
The modern theory of likelihood inference provides improved inferences in many parametric models, with little more effort than is required for application of standard first-order theory. We outline the relevant computations, and illustrate the calculations ...
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...
The spectral density function plays a key role in fitting the tail of multivariate extremal data and so in estimating probabilities of rare events. This function satisfies moment constraints but unlike the univariate extreme value distributions has no simple ...
