Comparison of robust estimation and Kalman filtering applied to fingertip tracking in human-machine interfaces
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Generalized Linear Models have become a commonly used tool of data analysis. Such models are used to fit regressions for univariate responses with normal, gamma, binomial or Poisson distribution. Maximum likelihood is generally applied as fitting method. I ...
The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and smoothing in the fields of linear Gaussian state-space models. In its standard setting it has a simple recursive form which implies high computational effici ...
In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...
It has been shown previously that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition (here we mean both identification and verification). Rec ...
The number of vehicles included in a metered motorway ramp or an urban signalized link at any time is valuable information for real-time control. A Kalman-Filter is employed to produce reliable estimates of this quantity based on real-time measurements of ...
Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development ...
In this paper, we present a particle filter that exploits multi modal information for robust target tracking. We demonstrate a Bayesian framework for combining acoustic and video information using a state space approach. A proposal strategy for joint acous ...
A proposal function determines the random particle support of a particle filter. When this support is distributed close to the true target density, filter's estimation performance increases for a given number of particles. In this paper, a proposal strateg ...
In this paper, we propose a robust filtering approach to distributed power allocation. A robust filter is used to predict the channel gain and interference under incomplete knowledge of the shadowing coefficient. Simulation results show the superior perfor ...
The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and smoothing in the fields of linear Gaussian state-space models. In its standard setting it has a simple recursive form which implies high computational effici ...
