290 citations · 681 across the 38 of their papers we have counts for
25 papers · 1 filter
Fixed-structure Gaussian Mixture Filtering with Robust Measurement Updates under Outliers
Ondřej Straka, Uwe D. Hanebeck
Bayesian state estimation for discrete-time nonlinear stochastic systems is considered in the presence of measurement outliers. Building on a fixed-structure Gaussian mixture filte…
Sampling-based Model Predictive Control Using Trust Regions
Markus Walker, Marcel Reith-Braun, Daniel Frisch +1
Sampling-based model predictive control (MPC) algorithms, such as model predictive path integral (MPPI), enable approximate, gradient-free solutions to optimal control problems by…
Smooth Sampling-Based Model Predictive Control Using Deterministic Samples
Markus Walker, Marcel Reith-Braun, Tai Hoang +2
Sampling-based model predictive control (MPC) is effective for nonlinear systems but often produces non-smooth control inputs due to random sampling. To address this issue, we exte…
Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Markus Walker, Daniel Frisch, Uwe D. Hanebeck
Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance o…
Efficient Gaussian Mixture Filters based on Transition Density Approximation
Ondŕej Straka, Uwe D. Hanebeck
Gaussian mixture filters for nonlinear systems usually rely on severe approximations when calculating mixtures in the prediction and filtering step. Thus, offline approximations of…
Deterministic Sampling on the Circle using Projected Cumulative Distributions
Daniel Frisch, Uwe D. Hanebeck
We propose a method for deterministic sampling of arbitrary continuous angular density functions. With deterministic sampling, good estimation results can typically be achieved wit…
Deterministic Sampling of Multivariate Densities based on Projected Cumulative Distributions
Uwe D. Hanebeck
We want to approximate general multivariate probability density functions by deterministic sample sets. For optimal sampling, the closeness to the given continuous density has to b…
A Distance-based Framework for Gaussian Processes over Probability Distributions
Maxim Dolgov, Uwe D. Hanebeck
Gaussian processes constitute a very powerful and well-understood method for non-parametric regression and classification. In the classical framework, the training data consists of…
FLUX: Progressive State Estimation Based on Zakai-type Distributed Ordinary Differential Equations
Uwe D. Hanebeck
We propose a homotopy continuation method called FLUX for approximating complicated probability density functions. It is based on progressive processing for smoothly morphing a giv…
High-Accuracy Real-Time Whole-Body Human Motion Tracking Based on Constrained Nonlinear Kalman Filtering
Jannik Steinbring, Christian Mandery, Nikolaus Vahrenkamp +2
We present a new online approach to track human whole-body motion from motion capture data, i.e., positions of labeled markers attached to the human body. Tracking in noisy data ca…
Infinite-horizon Linear Optimal Control of Markov Jump Systems without Mode Observation via State Feedback
Maxim Dolgov, Uwe D. Hanebeck
In this paper, we consider stochastic optimal control of Markov Jump Linear Systems with state feedback but without observation of the jumping parameter. The proposed control law i…
The Smart Sampling Kalman Filter with Symmetric Samples
Jannik Steinbring, Martin Pander, Uwe D. Hanebeck
Nonlinear Kalman Filters are powerful and widely-used techniques when trying to estimate the hidden state of a stochastic nonlinear dynamic system. In this paper, we extend the Sma…
Recursive Bayesian Filtering in Circular State Spaces
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
For recursive circular filtering based on circular statistics, we introduce a general framework for estimation of a circular state based on different circular distributions, specif…
Optimal Reduction of Multivariate Dirac Mixture Densities
Uwe D. Hanebeck
This paper is concerned with the optimal approximation of a given multivariate Dirac mixture, i.e., a density comprising weighted Dirac distributions on a continuous domain, by an…
Truncated Moment Problem for Dirac Mixture Densities with Entropy Regularization
Uwe D. Hanebeck
We assume that a finite set of moments of a random vector is given. Its underlying density is unknown. An algorithm is proposed for efficiently calculating Dirac mixture densities…
Unscented Orientation Estimation Based on the Bingham Distribution
Igor Gilitschenski, Gerhard Kurz, Simon J. Julier +1
Orientation estimation for 3D objects is a common problem that is usually tackled with traditional nonlinear filtering techniques such as the extended Kalman filter (EKF) or the un…
Recursive Estimation of Orientation Based on the Bingham Distribution
Gerhard Kurz, Igor Gilitschenski, Simon Julier +1
Directional estimation is a common problem in many tracking applications. Traditional filters such as the Kalman filter perform poorly because they fail to take the periodic nature…
Extended Object Tracking with Random Hypersurface Models
Marcus Baum, Uwe D. Hanebeck
The Random Hypersurface Model (RHM) is introduced that allows for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the s…
The Kernel-SME Filter for Multiple Target Tracking
Marcus Baum, Uwe D. Hanebeck
We present a novel method called Kernel-SME filter for tracking multiple targets when the association of the measurements to the targets is unknown. The method is a further develop…
Optimal Sequence-Based Control and Estimation of Networked Linear Systems
Jörg Fischer, Marc Reinhardt, Uwe D. Hanebeck
In this paper, a unified approach to sequence-based control and estimation of linear networked systems with multiple sensors is proposed. Time delays and data losses in the control…
Optimal Sequence-Based LQG Control over TCP-like Networks Subject to Random Transmission Delays and Packet Losses
Jörg Fischer, Achim Hekler, Maxim Dolgov +1
This paper addresses the problem of sequence-based controller design for Networked Control Systems (NCS), where control inputs and measurements are transmitted over TCP-like networ…
Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs
Achim Hekler, Jörg Fischer, Uwe D. Hanebeck
In this paper, we address the problem of controlling a system over an unreliable connection that is affected by time-varying delays and randomly occurring packet losses. A novel se…
Progressive Gaussian Filtering
Uwe D. Hanebeck, Jannik Steinbring
In this paper, we propose a progressive Bayesian procedure, where the measurement information is continuously included into the given prior estimate (although we perform observatio…
Robust Filtering and Smoothing with Gaussian Processes
Marc Peter Deisenroth, Ryan Turner, Marco F. Huber +2
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function…