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- Ángel F. García-Fernández, Mark R. Morelande, Jesús Grajal
- 14th International Conference on Information…
- 2011

This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in… (More)

- Ángel F. García-Fernández, Mark R. Morelande, Jesús Grajal
- IEEE Transactions on Signal Processing
- 2012

We devise a filtering algorithm to approximate the first two moments of the posterior probability density function (PDF). The novelties of the algorithm are in the update step. If the likelihood has a bounded support, we can use a modified prior distribution that meets Bayes' rule exactly. Applying a Kalman filter (KF) to the modified prior distribution,… (More)

- Ángel F. García-Fernández, Jesús Grajal
- Signal Processing
- 2011

- Ángel F. García-Fernández, Lennart Svensson, Mark R. Morelande, Simo Särkkä
- IEEE Transactions on Signal Processing
- 2015

This paper is concerned with Gaussian approximations to the posterior probability density function (PDF) in the update step of Bayesian filtering with nonlinear measurements. In this setting, sigma-point approximations to the Kalman filter (KF) recursion are widely used due to their ease of implementation and relatively good performance. In the update step,… (More)

- Ángel F. García-Fernández, Lars Hammarstrand, Maryam Fatemi, Lennart Svensson
- IEEE Transactions on Intelligent Transportation…
- 2014

This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors, and an inertial measurement unit. We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a… (More)

- Ángel F. García-Fernández, Mark R. Morelande, Jesús Grajal
- 2012 15th International Conference on Information…
- 2012

This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule… (More)

- Ángel F. García-Fernández, Jesús Grajal, Mark R. Morelande
- IEEE Trans. Aerospace and Electronic Systems
- 2013

- Mark R. Morelande, Ángel F. García-Fernández
- IEEE Transactions on Signal Processing
- 2013

A theoretical analysis is presented of the correction step of the Kalman filter (KF) and its various approximations for the case of a nonlinear measurement equation with additive Gaussian noise. The KF is based on a Gaussian approximation to the joint density of the state and the measurement. The analysis metric is the Kullback-Leibler divergence of this… (More)

- Ángel F. García-Fernández, Mark R. Morelande, Jesús Grajal
- IEEE Transactions on Signal Processing
- 2011

This paper addresses the problem of simultaneously localizing multiple targets and estimating the positions of the sensors in a sensor network using particle filters. We develop a new technique called multitarget simultaneous localization and mapping (MSLAM) that has better performance than the well-known FastSLAM when there are several targets in the… (More)

- Ángel F. García-Fernández, Jesús Grajal
- 2009 12th International Conference on Information…
- 2009

This paper addresses the problem of detecting and tracking multiple targets in a Bayesian framework. First, we introduce the definition of Joint MultitracK Probability Density (JMKPD) which is the probability of having a certain number of tracks, each one clearly identified with an ID number, and a kinematic state. We develop the a priori model needed to… (More)