High-Quality Assumed Gaussian Filtering for Nonlinear Systems

Forschungsthema/Bereich

Probabilistic machine learning, State estimation

Typ der Abschlussarbeit

Bachelor / Master

Startzeitpunkt

-

Bewerbungsschluss

30.06.2026

Dauer der Arbeit

6 months

Beschreibung

We consider the general state estimation problem for a discrete-time stochastic nonlinear dynamic system with noisy measurements. Specifically, we focus on Gaussian filters that approximate the true, in general complex, state Probability Density Function (PDF) by explicitly optimizing the shape of a Gaussian distribution after each processing step. This class of filters is known as Gaussian Assumed Density Filters (GADFs).

At ISAS, we have developed a broad spectrum of GADFs, ranging from Linear Regression Kalman Filters (LRKFs) and Progressive Gaussian Filters (PGFs) to Inverse Gaussian Process (IGP) interpolation methods. Compared to the former approaches, these IGP interpolation filtering techniques exhibit significantly improved performance, driven by deterministic sampling of the joint density of measurements and states, as well as the effective exploitation of these samples using machine-learning–based methods.

The goal of this thesis is to build upon the core ideas of these existing methods to design a novel algorithm and evaluate its performance against state-of-the-art techniques. The work will roughly comprise the following tasks:
● Literature research on nonlinear filtering methods,
● Familiarization with the Julia programming language,
● Development and design of a novel algorithm,
● Implementation and integration of the methods in Julia,
● Comparison with other state-of-the-art-methods.

Voraussetzung

Voraussetzungen an Studierende

• Strong self-motivation, reliability, and critical mind are expected.

Studiengangsbereiche

• Ingenieurwissenschaften
  Elektrotechnik & Informationstechnik
  Geodäsie & Geoinformatik
  Informatik
  Maschinenbau
  Mechatronik & Informationstechnik
  Mobilität und Infrastruktur
  Mechanical Engineering
  Mobility Systems Engineering and Management
  Remote Sensing and Geoinformatics
  Information System Engineering and Management
  Computer Science
  Electrical Engineering and Information Technology
  Mechatronics and Information Technology
  Medizintechnik
• Naturwissenschaften und Technik
  Mathematik
  Physik
  Technomathematik
  Computational and Data Sience
  Physics
  Wirtschaftsmathematik
• Wirtschafts- und Rechtswissenschaften
  Wirtschaftsinformatik
  Wirtschaftsingenieurwesen

Betreuung

Titel, Vorname, Name

Jiachen Zhou

Organisationseinheit

Institut für Anthropomatik und Robotik (IAR) - Intelligent Sensor-Actuator-Systems (ISAS)

E-Mail Adresse

jiachen.zhou@kit.edu

Link zur eigenen Homepage/Personenseite

Website

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E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an jiachen.zhou@kit.edu


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