[HTML][HTML] Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond
Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …
devoted to time series state space models for a large variety of dynamic estimation …
A look at Gaussian mixture reduction algorithms
We review the literature and look at two of the best algorithms for Gaussian mixture
reduction, the GMRC (Gaussian Mixture Reduction via Clustering) and the COWA …
reduction, the GMRC (Gaussian Mixture Reduction via Clustering) and the COWA …
Finite mixture modeling in time series: A survey of Bayesian filters and fusion approaches
From the celebrated Gaussian mixture, model averaging estimators to the cutting-edge multi-
Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible …
Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible …
[HTML][HTML] Approximate representations for multi-robot control policies that maximize mutual information
We address the problem of controlling a small team of robots to estimate the location of a
mobile target using non-linear range-only sensors. Our control law maximizes the mutual …
mobile target using non-linear range-only sensors. Our control law maximizes the mutual …
Product importance sampling for light transport path guiding
Abstract The efficiency of Monte Carlo algorithms for light transport simulation is directly
related to their ability to importance‐sample the product of the illumination and reflectance in …
related to their ability to importance‐sample the product of the illumination and reflectance in …
Stochastic monitoring of distribution networks including correlated input variables
The evolving complexity of distribution networks with higher levels of uncertainties is a new
challenge faced by system operators. This paper introduces the use of Gaussian mixtures …
challenge faced by system operators. This paper introduces the use of Gaussian mixtures …
Discrete and continuous, probabilistic anticipation for autonomous robots in urban environments
F Havlak, M Campbell - IEEE Transactions on Robotics, 2013 - ieeexplore.ieee.org
This paper develops a probabilistic anticipation algorithm for dynamic objects observed by
an autonomous robot in an urban environment. Predictive Gaussian mixture models are …
an autonomous robot in an urban environment. Predictive Gaussian mixture models are …
On generalized covariance intersection for distributed PHD filtering and a simple but better alternative
T Li, JM Corchado, S Sun - 2017 20th International Conference …, 2017 - ieeexplore.ieee.org
Some concerns are raised on the prevailing generalized covariance intersection (GCI)
based Gaussian mixture probability hypothesis density (GM-PHD) fusion for distributed …
based Gaussian mixture probability hypothesis density (GM-PHD) fusion for distributed …
Road intensity based mapping using radar measurements with a probability hypothesis density filter
C Lundquist, L Hammarstrand… - IEEE Transactions on …, 2010 - ieeexplore.ieee.org
Mapping stationary objects is essential for autonomous vehicles and many autonomous
functions in vehicles. In this contribution the probability hypothesis density (PHD) filter …
functions in vehicles. In this contribution the probability hypothesis density (PHD) filter …
Gaussian mixture reduction via clustering
D Schieferdecker, MF Huber - 2009 12th international …, 2009 - ieeexplore.ieee.org
Recursive processing of Gaussian mixture functions inevitably leads to a large number of
mixture components. In order to keep the computational complexity at a feasible level, the …
mixture components. In order to keep the computational complexity at a feasible level, the …