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Introduction to Unscented Kalman Filtering

version 1.2.0.0 (2.29 MB) by Krisada Sangpetchsong
Unscented Kalman filtering tutorial: Simulink and tilt sensor case study.

7.8K Downloads

Updated 05 Aug 2009

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This engineering note is the first of two parts:

Part 1 Design and Simulation.
Part 2 Real-World System Realization. (Being written)

It aims at demonstrating how you may use Matlab/Simulink together with Rapid STM32 blockset and ARM Cortex-M3 processors (STM32) to develop digital signal processing systems; using a tilt sensor as a case study.

It covers the development process from design, simulation, hardware-in-the-loop testing, and creating a stand-alone embedded system. The content is supposed to be as simple/introductory as possible.

In this first part:

1. The motivation for using Simulink for embedded system development is explained.
2. A simplified model of tilt sensor system is developed.
3. Kalman filtering and Unscented Kalman filtering (UKF) theory is summarized.
4. Graphical instructions are then provided to guide you through the whole process of implementing a Simulink model to design, simulate, and evaluate the performance of an UKF for a tilt sensor system.

Note: Source code is also provided to perform Monte Carlo simulation based on Simulink model to evaluate UKF performance using covariance analysis.

In the second part, graphical instructions will be provided to guide you through the process of transferring your design from Simulink model to real-world stand-alone tilt sensor system based on Rapid STM32 - R1 Stamp board.

Visit www.rapidstm32.com for more information.

Cite As

Krisada Sangpetchsong (2021). Introduction to Unscented Kalman Filtering (https://www.mathworks.com/matlabcentral/fileexchange/24917-introduction-to-unscented-kalman-filtering), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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