A low cost MEMS and complementary filter based attitude heading reference system (AHRS) for low speed aircraft

Islam, Md. Saiful; Shajid-Ul-Mahmud, Md.; Tariqul, Islam; Amin, Md. Syedul; Hossam-E-Haider,

doi:10.1109/ceeict.2016.7873047

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…In addition, A max and A min are the maximum and minimum accelerations measured in a single stride, respectively. The attitude heading reference system (AHRS) is a method applying a built-in accelerometer, gyroscope, and magnetometer in the IMU to calculate the attitude and heading expressed in Euler angles (φ, θ, ψ) [28]. The heading described by the Euler angle yaw (ψ) is obtained from AHRS as follows:…”

Section: B Nlos Identificationmentioning

confidence: 99%

NLOS Identification Based UWB and PDR Hybrid Positioning System

Kim

Pyun

2021

IEEE Access

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Some of the researches on indoor positioning have been conducted, but there are still many constraints on indoor positioning approaches. Among these approaches, ultra-wideband (UWB) provides a fast and precise positioning performance but requires a sufficient infrastructure and a clear line-ofsight (LOS) channel. However, inertial sensor-based pedestrian dead reckoning (PDR) operates without infrastructure, but it requires position initialization and has error drift problems. In this study, we propose a hybrid positioning system that fully combines UWB and PDR to overcome such constraints and improve the positioning performance. This hybrid positioning system uses a Kalman filter (KF) based fusion method that identifies non-line-of-sight (NLOS) environments and mitigates UWB errors through PDR. We also evaluated the proposed system implemented using practical testbed devices at indoor environments classified as LOS, weak NLOS, and hard NLOS. The evaluation results showed that the proposed system significantly improves the positioning performance and alleviates the positioning constraints, as compared to the single positioning system. Our system has been designed to be lightweight compared to the existing extended KF-based convergence system, but is more robust to both weak and hard NLOS environments. Eventually, it improved positioning performance by 35.5% than existing hybrid systems in the hard NLOS environments. INDEX TERMSIndoor positioning, ultra-wideband (UWB), pedestrian dead-reckoning (PDR), Kalman filter (KF) sensor fusion, and hybrid positioning.

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Section: B Nlos Identificationmentioning

confidence: 99%

NLOS Identification Based UWB and PDR Hybrid Positioning System

Kim

Pyun

2021

IEEE Access

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“…As discussed, this can not be guaranteed when referring to smartphone sensors. However, as exact cut-off frequencies are not necessarily required for gyroscope/accelerometer fusion, real-world setups often refer to a simpler form of (2.41), inspired by IIR filters, satisfying (2.42), and mitigating aforementioned requirements [Isl+16] y Figure 2.12: Layout and behavior of a complementary filter which combines the values provided by an accelerometer and a gyroscope. After converting their readings to absolute rotations, the converted results are applied to a low-pass and a high-pass filter, which are complementary to each other.…”

Section: Turn-detectionmentioning

confidence: 99%

Smartphone-Based 3D Indoor Localization and Navigation

Ebner¹

2021

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During my master's thesis, which covered the essentials of Wi-Fi location estimation, I came in contact with the concepts of indoor localization, and multi sensor fusion. Having attracted my interest, I hereafter joined the Faculty of Computer Science and Business Information Systems at the University of Applied Sciences Würzburg-Schweinfurt as a research assistant. During that time I conducted the majority of my research on indoor localization and navigation, which finally lead to writing this work.I would like to thank my colleagues during that time, but especially my local doctoral adviser Prof. Dr. Frank Deinzer. Not only did he guide me towards earning a doctors degree, he also provided extensive feedback to all publications, always ensuring utmost scientific quality. I would also like to thank my external doctoral adviser and first referee Prof. Dr. habil. Marcin Grzegorzek for his trust, all provided feedback, and the warm welcome within his research group. I also owe gratitude to my co-workers Lukas, Toni, and Markus. Without hours of mutual feedback, technical input, paired programming, data acquisition, and networking, this work would not have been possible. Likewise, I also wish to thank Prof. Dr. Karsten Huffstadt and Prof. Dr. Arndt Balzer for helping with funding my initial employment.In order to develop a generic indoor localization and navigation solution, the presented system was deployed to and tested within several unique buildings. For this, special thanks go to Dr. Hellmuth Möhring from the RothenburgMuseum in Rothenburg ob der Tauber, Prof. Dr. Günther Moosbauer from the Gäubodenmuseum in Straubing, Angelika Schreiber from the Hutmuseum in Lindenberg, the Landesstelle für die nichtstaatlichen Museen in Bayern, and the Sparkassenstiftung. By providing access and enabling temporal hardware installations they allowed gathering data within a range of unique and representative public buildings, enhancing the quality of the developed system, and the experiments provided within this work.Thanks also go to Markus, Christin, Alexander, and Maximilian, not only for spending numerous hours of their spare time with proof reading and feedback, but also for all emotional support while conducting my research and writing this work. The same goes to my parents, for always supporting me, and my education.To Katharina, who has been a true friend, and will never be forgotten.

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“…Research studies on designing an accurate AHRS with minimum error could be categorized in three main topics, namely complementary filters (CF), optimization approaches, and Kalman filter (KF)-based solutions. The authors of [3,4] have considered AHRS improvement using CF and MEMS sensors. A more completed AHS was designed and implemented by the authors of [5] based on explicit CF, which showed great capability for implementation in embedded hardware [5].…”

Section: Related Workmentioning

confidence: 99%

“…For the measurement model, the magnetometer and acceleration data are considered as the EKF measurement, as defined in Equation (4). H k is the measurement matrix, and z k consists of two elements, namely the normalized specific force and normalized horizontal component of the geomagnetic field.…”

Section: Ekf Model and Parametersmentioning

confidence: 99%

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

Farhangian

Landry

2020

Sensors

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Accurate attitude and heading reference system (AHRS) play an essential role in navigation applications and human body tracking systems. Using low-cost microelectromechanical system (MEMS) inertial sensors and having accurate orientation estimation, simultaneously, needs optimum orientation methods and algorithms. The error of attitude estimation may lead to imprecise navigation and motion capture results. This paper proposed a novel intermittent calibration technique for MEMS-based AHRS using error prediction and compensation filter. The method, inspired from the recognition of gyroscope’s error and by a proportional integral (PI) controller, can be regulated to increase the accuracy of the prediction. The experimentation of this study for the AHRS algorithm, aided by the proposed prediction filter, was tested with real low-cost MEMS sensors consists of accelerometer, gyroscope, and magnetometer. Eventually, the error compensation was performed by post-processing the measurements of static and dynamic tests. The experimental results present about 35% accuracy improvement in attitude estimation and demonstrate the explicit performance of proposed method.

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A low cost MEMS and complementary filter based attitude heading reference system (AHRS) for low speed aircraft

Cited by 17 publications

References 6 publications

NLOS Identification Based UWB and PDR Hybrid Positioning System

NLOS Identification Based UWB and PDR Hybrid Positioning System

Smartphone-Based 3D Indoor Localization and Navigation

Accuracy Improvement of Attitude Determination Systems Using EKF-Based Error Prediction Filter and PI Controller

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