Distributed Kalman Filtering for Large-Scale Dynamic Systems with Sparsely Coupled States

Tutor: Henning Paul, Shengdi Wang
Type of Thesis: Master's thesis (MSc)
date of end: 09/2018
Student: Lukas Zumvorde
Status: finished


Kalman filtering is an approach which is used in many control systems to provide a state estimate for a subsequent controller. It obtains the optimal solution (in the sense of an optimization problem) by combining predictions using a system model with the observations on hidden state variables measured by sensors. In a large system, the dimension of the state and the number of sensors will be large. Using a fusion center in the network combine all observations and perform centralized Kalman filtering will be impractical because of long communication distances and high computational effort. Moreover, it poses a single point of failure and latency which will also restrict the performance of centralized scheme. In this work, our motivation is to distribute the global system into low dimensional, inter-coupled sensor-based subsystems and design a distributed Kalman filter correspondingly.


The main objective of this thesis is to:

  1. Research and investigate methods from the state of the art in order to decompose the whole large-scale dynamic system into small subsystems;

  2. investigate one distributed Kalman filter algorithm to perform state estimation locally at each subsystem with information exchange between subsystems;

  3. achieve a consensus-based estimation result with respect to each state variable.

The student should firstly study how the a centralized Kalman filter works in a large-scale system. Then research on some data fusion methods including sensor network topology design and consensus approach will be needed with the purpose on reducing the communication requirement.

Last change on 01.10.2018 by S. Wang
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