State estimation under quantized measurements: A Sigma-Point Bayesian approach. Abstract: Sensors providing only quantized or binary measurements are present ...

State estimation under quantized measurements: a Sigma-Point Bayesian approach. Costanzo Manes and Francesco Martinelli. Abstract—Sensors providing only ...

An algorithm which merges some concepts of the Unscented Kalman Filter with some aspects of the Particle Filter with the main advantage of the proposed ...

The main advantage of the proposed algorithm with respect to a PF is that much less particles are needed. Moreover, the way to generate particles in the ...

Costanzo Manes , Francesco Martinelli: State estimation under quantized measurements: A Sigma-Point Bayesian approach. CDC 2013: 5024-5029.

An algorithm is described which estimates the state of a linear system from quantized measurements of the output of that system. The estimator is an ...

Nonlinear Kalman filters are algorithms that approximately solve the Bayesian filtering problem by employing the measurement.

Manes et al. State estimation under quantized measurements: a sigma-point Bayesian approach. Proceedings of the 52nd IEEE Conference on Decision and Control.

Probabilistic inference is the problem of estimating the hidden variables (states or param- eters) of a system in an optimal and consistent fashion (using ...

Bayesian state estimation is a flexible framework to address relevant problems at the heart of existing and upcoming technologies.