Analysis of performance of Attitude & Heading Reference System (AHRS) motion sensor algorithms through Bluetooth Low Energy (BLE) in nRF52840 System-On-Chip (SOC)

This research report investigates the performance of three distinct motion sensor algorithms, namely NXP, Madgwick, and Mahony, implemented on the Nordic NRF52840 System-on-Chip (SOC) through Bluetooth Low Energy (BLE) communication. The study utilizes the Adafruit Feather Sense board as the experim...

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Bibliographic Details
Main Author: Chung, Benjamin Zhi Yong
Other Authors: Oh Hong Lye
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175252
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Institution: Nanyang Technological University
Language: English
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Summary:This research report investigates the performance of three distinct motion sensor algorithms, namely NXP, Madgwick, and Mahony, implemented on the Nordic NRF52840 System-on-Chip (SOC) through Bluetooth Low Energy (BLE) communication. The study utilizes the Adafruit Feather Sense board as the experimental platform for evaluating the algorithms' efficacy in providing accurate motion tracking and orientation estimation. The research methodology involves the implementation of each algorithm on the NRF52840 SOC, utilizing the board's integrated motion sensors. The algorithms' performance is assessed based on several key metrics, including drift correction, response time and computational efficiency. The study also explores the impact of BLE communication on the transmission of the orientation data, considering the constraints and capabilities of the NRF52840 SOC. Through a comprehensive analysis of the NXP, Madgwick, and Mahony algorithms, this research aims to provide valuable insights into their suitability for real-world applications, particularly in scenarios where Bluetooth Low Energy is a crucial communication medium. The findings of this study contribute to the understanding of the trade-offs involved in selecting a motion sensor algorithm for BLE-enabled devices, with implications for applications such as wearable devices, Internet of Things (IoT) sensors, and other motion-sensitive devices.