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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, the authors 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, they propose a motion model based on the observations taken by an Inertial Measurement Unit, that takes into account its possibly biased behavior, and they show how heterogeneous sensors (using time-delay or received-signal-strength based ranging) can be combined in a recursive, online Bayesian estimation scheme.
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