6 papers
Learning Adaptive Parameter Policies for Nonlinear Bayesian Filtering
Ondrej Straka, Felipe Giraldo-Grueso, Renato Zanetti
For many nonlinear Bayesian state estimation problems, the posterior recursion is not analytically tractable, leading to algorithms that are influenced by numerical approximation e…
Kernel-Based Ensemble Gaussian Mixture Probability Hypothesis Density Filter
Dalton Durant, Renato Zanetti
In this work, a kernel-based Ensemble Gaussian Mixture Probability Hypothesis Density (EnGM-PHD) filter is presented for multi-target filtering applications. The EnGM-PHD filter co…
Deterministic Optimal Transport-based Gaussian Mixture Particle Filtering for Verifiable Applications
Andrey A Popov, Renato Zanetti
Mixture-model particle filters such as the ensemble Gaussian mixture filter require a resampling procedure in order to converge to exact Bayesian inference. Canonically, stochastic…
The Ensemble Epanechnikov Mixture Filter
Andrey A. Popov, Renato Zanetti
In the high-dimensional setting, Gaussian mixture kernel density estimates become increasingly suboptimal. In this work we aim to show that it is practical to instead use the optim…
Precision Mars Entry Navigation with Atmospheric Density Adaptation via Neural Networks
Felipe Giraldo-Grueso, Andrey A. Popov, Renato Zanetti
Spacecraft entering Mars require precise navigation algorithms capable of accurately estimating the vehicle's position and velocity in dynamic and uncertain atmospheric environment…
What are You Weighting For? Improved Weights for Gaussian Mixture Filtering With Application to Cislunar Orbit Determination
Dalton Durant, Andrey A. Popov, Renato Zanetti
This work focuses on the critical aspect of accurate weight computation during the measurement incorporation phase of Gaussian mixture filters. The proposed novel approach computes…