Bayesian filtering for location estimation (original) (raw)
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2003
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Effectiveness of Bayesian filters: An information fusion perspective
The general solution for dynamic state estimation is to model the system as a hidden Markov process and then employ a recursive estimator of the prediction–correction format (of which the best known is the Bayesian filter) to statistically fuse the time-series observations via models. The performance of the estimator greatly depends on the quality of the statistical mode assumed. In contrast, this paper presents a modeling-free solution, referred to as the observation-only (O 2) inference, which infers the state directly from the observations. A Monte Carlo sampling approach is correspondingly proposed for unbiased nonlinear O 2 inference. With faster computational speed, the performance of the O 2 inference has identified a benchmark to assess the effectiveness of conventional recursive estimators where an esti-mator is defined as effective only when it outperforms on average the O 2 inference (if applicable). It has been quantitatively demonstrated, from the perspective of information fusion, that a prior " biased " information (which inevitably accompanies inaccurate modelling) can be counterproductive for a filter, resulting in an ineffective estimator. Classic state space models have shown that a variety of Kalman filters and particle filters can easily be ineffective (inferior to the O 2 inference) in certain situations, although this has been omitted somewhat in the literature.
Sensors, 2020
Range sensors are currently present in countless applications related to perception of the environment. In mobile robots, these devices constitute a key part of the sensory apparatus and enable essential operations, that are often addressed by applying methods grounded on probabilistic frameworks such as Bayesian filters. Unfortunately, modern mobile robots have to navigate within challenging environments from the perspective of their sensory devices, getting abnormal observations (e.g., biased, missing, etc.) that may compromise these operations. Although there exist previous contributions that either address filtering performance or identification of abnormal sensory observations, they do not provide a complete treatment of both problems at once. In this work we present a statistical approach that allows us to study and quantify the impact of abnormal observations from range sensors on the performance of Bayesian filters. For that, we formulate the estimation problem from a generi...
2010
This paper shows the applicability of recently-developed Gaussian nonlinear filters to sensor data fusion for positioning purposes. After providing a brief review of Bayesian nonlinear filtering, we specially address square-root, derivative-free algorithms based on the Gaussian assumption and approximation rules for numerical integration, namely the Gauss--Hermite quadrature rule and the cubature rule. Then, we propose a motion model based on the observations taken by an Inertial Measurement Unit, that takes into account its possibly biased behavior, and we show how heterogeneous sensors (using time-delay or received-signal-strength based ranging) can be combined in a recursive, online Bayesian estimation scheme. These algorithms show a dramatic performance improvement and better numerical stability when compared to typical nonlinear estimators such as the Extended Kalman Filter or the Unscented Kalman Filter, and require several orders of magnitude less computational load when compared to Sequential Monte Carlo methods, achieving a comparable degree of accuracy.