prakharg01/steadystatekalmanfilter-labview

Various MEMS devices are available in market that have built in accelerometers, gyroscopes and magnetometers for measuring the orientation in space, but the measurements are not stable and can’t be used raw effectively. Kalman filter does a great job in playing the role of an observer that can accurately estimate the final state based on inaccurate and noisy measurement data. Here I propose to study one such Kalman filter - Steady state Kalman filter, that is used with the MPU9250 IMU – 9 axis sensor that has a built-in accelerometer, gyroscope and a magnetometer. Pitch angle is estimated at each timestep. STM32 is used to collect the IMU data through SPI interface, this data is sent serially to LabVIEW, where a VI is used to visualize the raw data and the estimated Kalman filter data.

Steady State Kalman Filter - LabVIEW

This repository contains an implementation of a Steady State Kalman Filter for orientation estimation using LabVIEW and STM32. The Kalman filter is utilized to accurately estimate the final state based on inaccurate and noisy measurement data from the MPU9250 IMU (Inertial Measurement Unit) - a 9-axis sensor that includes an accelerometer, gyroscope, and magnetometer. The goal of this project is to estimate the pitch angle at each timestep.

Overview

Various MEMS devices available in the market provide built-in accelerometers, gyroscopes, and magnetometers for measuring orientation in space. However, the raw measurements from these sensors are often unstable and cannot be effectively used as is. The Kalman filter serves as an observer that can accurately estimate the final state based on noisy and inaccurate measurement data.

In this project, the Steady State Kalman Filter is implemented using LabVIEW. The MPU9250 IMU is used to collect the sensor data, which is then transmitted to LabVIEW through the STM32 microcontroller via the SPI interface. LabVIEW, in turn, utilizes a VI (Virtual Instrument) to visualize both the raw data and the estimated data from the Kalman filter.

Demonstration

A demonstration of the Steady State Kalman Filter implementation can be found on YouTube at the following link: Steady State Kalman Filter Demo.

Usage

The expected input to the VISA (Virtual Instrument Software Architecture) driver is a comma-separated value sent through the serial port. The format of the input should be as follows: ax,ay,az,gx,gy,gz, where:

• ax: Accelerometer X-axis measurement
• ay: Accelerometer Y-axis measurement
• az: Accelerometer Z-axis measurement
• gx: Gyroscope X-axis measurement
• gy: Gyroscope Y-axis measurement
• gz: Gyroscope Z-axis measurement

Please make sure to configure the serial port settings and connections appropriately for communication between the STM32 and LabVIEW.

Note: This repo doesnt contain the source code to read imu values and send it over to the serial port, it builds on top of this functionality

Repository Contents

• report/: This contains the implementation details and the maths involved
• .vi_files: Includes the labview files

License

The code in this repository is licensed under the MIT License.

Acknowledgements

The Steady State Kalman Filter implementation for orientation estimation using LabVIEW and STM32 was developed by [Your Name]. If you find this project useful or have any questions, feel free to reach out.