Uncertainty modelling and computational aspects of data association

Abstract

A novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The systems of interest contain an unknown and varying number of dynamical objects that are partially observed under noisy and corrupted observations. In order to account for the lack of information about the different aspects of this type of complex system, an alternative representation of uncertainty based on possibility theory is considered. It is shown how analogues of usual concepts such as Markov chains and hidden Markov models (HMMs) can be introduced in this context. In particular, the considered statistical model for multiple dynamical objects can be formulated as a hierarchical model consisting of conditionally independent HMMs. This structure is leveraged to propose an efficient method in the context of Markov chain Monte Carlo (MCMC) by relying on an approximate solution to the corresponding filtering problem, in a similar fashion to particle MCMC. This approach is shown to outperform existing algorithms in a range of scenarios.

A Possibilistic Formulation of Autonomous Search for Targets

“…Because the last two theories are fairly complicated, and at present applicable only to discrete state spaces, we focus on possibility theory [21,22]. Recent research in nonlinear filtering and target tracking [23][24][25][26][27] have demonstrated that possibility theory provides an effective tool for uncertain knowledge representation and reasoning.…”

Possibility Generalized Labeled Multi-Bernoulli Filter for Multi-Target Tracking Under Epistemic Uncertainty

“…Then, we can set d 1 = 1 and d 0 = 1 − a to describe all the limited information, and nothing more. The set of false observations are modeled as an UFS Y , and its possibility function π F is defined by [30] π…”

Relación entre gestión de información y sistema de aseguramiento de la calidad en instituciones de educación superior. Una revisión

“…Al respecto, Laudon y Laudon (2014) sostienen que los datos necesitan ser organizados para que las personas los puedan entender y utilizar. Por su parte, Houssineau et al (2021) afirman que los datos adquieren relevancia solo gracias a su asociación. Al respecto, Milán et al (2011) opinan que es fundamental que los datos constituyan información, pues solo así tienen un sentido.…”