Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Piche, Robert"'
We propose modeling an angle-of-arrival (AOA) positioning measurement as a von Mises-Fisher (VMF) distributed unit vector instead of the conventional normally distributed azimuth and elevation measurements. Describing the 2-dimensional AOA measuremen
Externí odkaz:
http://arxiv.org/abs/1709.02437
The iterated posterior linearization filter (IPLF) is an algorithm for Bayesian state estimation that performs the measurement update using iterative statistical regression. The main result behind IPLF is that the posterior approximation is more accu
Externí odkaz:
http://arxiv.org/abs/1704.01113
Autor:
Piche, Robert
A linear Gaussian state-space smoothing algorithm is presented for estimation of derivatives from a sequence of noisy measurements. The algorithm uses numerically stable square-root formulas, can handle simultaneous independent measurements and non-e
Externí odkaz:
http://arxiv.org/abs/1610.04397
Publikováno v:
H. Nurminen, T. Ardeshiri, R. Pich\'e, and F. Gustafsson, "Skew-t Filter and Smoother with Improved Covariance Matrix Approximation", IEEE Transactions on Signal Processing, vol. 66, no. 21, pp. 5618-5633, 2018
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t-distributed measurement noise are proposed. The algorithms use a variational Bayes based posterior approximation with coupled location and skewness variables t
Externí odkaz:
http://arxiv.org/abs/1608.07435
Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t distributed measurement noise are presented. The proposed algorithms improve upon our earlier proposed filter and smoother using the mean field variational Bay
Externí odkaz:
http://arxiv.org/abs/1603.06216
Kalman filtering is a widely used framework for Bayesian estimation. The partitioned update Kalman filter applies a Kalman filter update in parts so that the most linear parts of measurements are applied first. In this paper, we generalize partitione
Externí odkaz:
http://arxiv.org/abs/1603.04683
Autor:
Raitoharju, Matti, Piché, Robert
Publikováno v:
in IEEE Aerospace and Electronic Systems Magazine, vol. 34, no. 10, pp. 2-19, 1 Oct. 2019
The Kalman filter and its extensions are used in a vast number of aerospace and navigation applications for nonlinear state estimation of time series. In the literature, different approaches have been proposed to exploit the structure of the state an
Externí odkaz:
http://arxiv.org/abs/1512.03077
Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-space models. The purpose of this article is to extend the twisted par
Externí odkaz:
http://arxiv.org/abs/1509.09175
Publikováno v:
IEEE Signal Processing Letters 22(11) (2015) 1898-1902
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal
Externí odkaz:
http://arxiv.org/abs/1503.06606
In this paper we present a new Kalman filter extension for state update called Partitioned Update Kalman Filter (PUKF). PUKF updates the state using multidimensional measurements in parts. PUKF evaluates the nonlinearity of the measurement function w
Externí odkaz:
http://arxiv.org/abs/1503.02857