A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices

Patonis, Photis; Πατιάς, Πέτρος; Tziavos, I. N.; Rossikopoulos, Dimitrios; Margaritis, Konstantinos G.

doi:10.3390/s18082616

Cited by 30 publications

(20 citation statements)

References 21 publications

(29 reference statements)

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“…AHRS provides the attitude and heading and thus, the direction cosine matrix C n b can be calculated uniquely if the rotation sequence of roll, pitch, and heading is pre-defined. Many approaches have been proposed for AHRS [22][23][24][25] using accelerometer, gyroscope, and magnetometer. Accelerometers provide roll and pitch, and magnetometers provide heading.…”

Section: Ahrsmentioning

confidence: 99%

Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer

Kwon

Shim

2020

Sensors

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The global positioning system (GPS) is an essential technology that provides positioning capabilities and is used in various applications such as navigation, surveying, mapping, robot simultaneous localization and mapping (SLAM), location-based service (LBS), etc. However, the GPS is known to be vulnerable to intentional attacks such as spoofing because of its simple signal structure. In this study, a direct method is proposed for GPS spoofing detection, using Attitude and Heading Reference System (AHRS) accelerometer and analyzing the detection performance with corresponding probability density functions (PDFs). The difference in the acceleration between the GPS receiver and the accelerometer is used to detect spoofing. The magnitude of the acceleration error may be used as a decision variable. Additionally, using the magnitude of the north (or east) component of the acceleration error as another decision variable is proposed, which shows better performance in some conditions. The performance of the two decision variables is compared by calculating the probability of spoofing detection and the detectable minimum spoofing acceleration (DMSA), given a pre-defined false alarm probability and a pre-defined detection probability. It turns out that both decision variables need to be used together to obtain the best spoofing detection performance.Sensors 2020, 20, 954 2 of 22 (MLE) [11]. Prior studies [7,12] summarize various spoofing detection techniques, among which low complexity spoofing detection techniques include C/N 0 monitoring, absolute power monitoring, power variation versus receiver movement, code and phase rate consistency check, and GPS clock consistency check, while high complexity spoofing detection techniques include Direction Of Arrival (DOA) comparison [14] and consistency check with other solutions [15][16][17][18].Among the effective high complexity spoofing detection techniques mentioned in the prior study [12], the DOA comparison approach uses the DOA measurements to assess the direction of the spoofing source and mitigates the interference by placing the spatial null in the antenna reception pattern [14]. The consistency check approach uses the inertial measurement unit (IMU) [15][16][17][18]. The relative platform trajectory estimated by the GNSS receiver is compared to the relative trajectory developed from the IMU measurement, specifically gyro output, to detect spoofing [15]. In References [16,17], a tightly coupled GPS / inertial navigation system (INS) is used to detect spoofing by incorporating fault detection concepts and Kalman filter, respectively, based on receiver autonomous integrity monitoring (RAIM). In [18], an accelerometer is used to detect spoofing by comparison of acceleration between the GPS receiver and the IMU. However, no prior research has been done on using the IMU and the acceleration error which is expressed with exact probability density function.In this study, a direct GPS spoofing detection method is proposed which uses attitude and heading reference system (AHRS)...

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Section: Ahrsmentioning

confidence: 99%

Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer

Kwon

Shim

2020

Sensors

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“…Even in static situations accelerometers are sensitive to vibration and mechanical noise. Gyroscopes are not free from noise but less sensitive for linear motions as accelerometers and not influenced by external influences as magnetometers [15].…”

Section: Robot Controlmentioning

confidence: 99%

“…This result to, that the additive error will rise after a few seconds due to the integration, which will make the computed angle unusable. That is why a sensor fusion is necessary [15].…”

Section: Robot Controlmentioning

confidence: 99%

“…To identify the yaw-angle the magnetometer, which measures the magnetic field on the earth, can be used. To avoid the mentioned disadvantages, a complementary filter is used to combine the accelerometer and magnetometer signals (for low frequency tilt angle calculation) with the gyroscope signals (for high-frequency tilt angle calculation) [15].…”

Section: Robot Controlmentioning

confidence: 99%

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Standardized data formats for flexible human-robot-collaboration at a multi-workstation assembly table

Brand

Benthin

Bohl

2019

Journal of Mechanical and Energy Engineering

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Abstract:In the process of industrial automatization, collaborative robots are becoming an increasingly important tool in a wide range of industries. The robot assists humans in strenuous and repetitive tasks, in order to increase the productivity and well-being of the worker. The concept presented in this paper focuses on a flexible assistance function of a robot for multiple workstations simultaneously and incorporates different human-machine-interaction approaches. Therefore, an assembly table (Figure 2) with the possibility to study different concepts for one robot at the same time is designed and constructed. Aspects like ergonomics and safety are considered in the design. Additionally, a standardized data format that enables users and developers to control the robot through peripheral devices is created. As an example, gesture and voice control are discussed and evaluated.

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“…Perbedaan penelitian ini dengan penelitian sebelumnya terletak pada step filtering yang digunakan. Pada penelitian sebelumnya [10][11], step filtering yang digunakan adalah 1 step Kalman Filter. Pada penelitian ini penulis tertarik melakukan penelitian 2 step Kalman filter untuk mengekstrak data dari accelerometer.…”

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Implementasi Algoritma 2 Step Kalman Filter Untuk Mengurangi Noise Pada Estimasi Data Accelerometer

Putra

2019

j-sakti

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An accelerometer is a useful sensor in technological development. Currently, the accelerometer is found on smartphone devices, navigation devices, and wearable devices. However, processing the sensor output signal into data that can be interpreted is not easy. This is because the output of an accelerometer sensor has significant noise. In this study, the authors are interested in developing an estimation method using a Kalman Filter. Kalman filter is an estimator so it is expected that the sensor data are more resistant to noise interference. In this study, the author innovated the 2 step Kalman filter. The study was conducted because the use of 1 step still has noise on the estimation results. Based on the analysis of the algorithm simulation results, it can be concluded that the Kalman filter 2-step algorithm has good performance in estimating the accelerometer sensor output. When compared with the Kalman filter 1 step algorithm, the Kalman filter 2 step algorithm has a smaller average error estimation and is able to achieve a constant/stable condition faster than the Kalman filter 1 step method

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A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices

Cited by 30 publications

References 21 publications

Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer

Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer

Standardized data formats for flexible human-robot-collaboration at a multi-workstation assembly table

Implementasi Algoritma 2 Step Kalman Filter Untuk Mengurangi Noise Pada Estimasi Data Accelerometer

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