A Parallel Kalman Filter for Estimation of Magnetic Disturbance and Orientation Based on Nine-axis Inertial/Magnetic Sensor Signals

2016 ◽  
Vol 40 (7) ◽  
pp. 659-666 ◽  
Author(s):  
Jung Keun Lee
Keyword(s):  
Kalman Filter ◽  
Magnetic Sensor ◽  
Magnetic Disturbance ◽  
Sensor Signals

scholarly journals Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4105 ◽  
Author(s):  
Qiuying Wang ◽  
Juan Yin ◽  
Aboelmagd Noureldin ◽  
Umar Iqbal
Keyword(s):  
Kalman Filter ◽  
Gradient Descent ◽  
Rapid Development ◽  
Alignment Algorithm ◽  
Magnetic Disturbance ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Positioning Systems ◽  
Descent Algorithm ◽  
Gradient Descent Algorithm

Foot-mounted Inertial Pedestrian-Positioning Systems (FIPPSs) based on Micro Inertial Measurement Units (MIMUs), have recently attracted widespread attention with the rapid development of MIMUs. The can be used in challenging environments such as firefighting and the military, even without augmenting with Global Navigation Satellite System (GNSS). Zero Velocity Update (ZUPT) provides a solution for the accumulated positioning errors produced by the low precision and high noise of the MIMU, however, there are some problems using ZUPT for FIPPS, include fast-initial alignment and unobserved heading misalignment angle, which are addressed in this paper. Our first contribution is proposing a fast-initial alignment algorithm for foot-mounted inertial/magnetometer pedestrian positioning based on the Adaptive Gradient Descent Algorithm (AGDA). Considering the characteristics of gravity and Earth’s magnetic field, measured by accelerometers and magnetometers, respectively, when the pedestrian is standing at one place, the AGDA is introduced as the fast-initial alignment. The AGDA is able to estimate the initial attitude and enhance the ability of magnetic disturbance suppression. Our second contribution in this paper is proposing an inertial/magnetometer positioning algorithm based on an adaptive Kalman filter to solve the problem of the unobserved heading misalignment angle. The algorithm utilizes heading misalignment angle as an observation for the Kalman filter and can improve the accuracy of pedestrian position by compensating for magnetic disturbances. In addition, introducing an adaptive parameter in the Kalman filter is able to compensate the varying magnetic disturbance for each ZUPT instant during the walking phase of the pedestrian. The performance of the proposed method is examined by conducting pedestrian test trajectory using MTi-G710 manufacture by XSENS. The experimental results verify the effectiveness and applicability of the proposed method.

Kalman filter-based yaw angle estimation by fusing inertial and magnetic sensing: a case study using low cost sensors

Sensor Review ◽  
2015 ◽  
Vol 35 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Pedro Neto ◽  
Nuno Mendes ◽  
A. Paulo Moreira
Keyword(s):  
Kalman Filter ◽  
Inertial Sensor ◽  
Low Cost ◽  
Human Robot Interaction ◽  
Magnetic Sensor ◽  
Robot Interaction ◽  
Magnetic Sensing ◽  
Content Type ◽  
Reliable Estimation ◽  
Yaw Angle

Purpose – The purpose of this paper is to achieve reliable estimation of yaw angles by fusing data from low-cost inertial and magnetic sensing. Design/methodology/approach – In this paper, yaw angle is estimated by fusing inertial and magnetic sensing from a digital compass and a gyroscope, respectively. A Kalman filter estimates the error produced by the gyroscope. Findings – Drift effect produced by the gyroscope is significantly reduced and, at the same time, the system has the ability to react quickly to orientation changes. The system combines the best of each sensor, the stability of the magnetic sensor and the fast response of the inertial sensor. Research limitations/implications – The system does not present a stable behavior in the presence of large vibrations. Considerable calibration efforts are needed. Practical implications – Today, most of human–robot interaction technologies need to have the ability to estimate orientation, especially yaw angle, from small-sized and low-cost sensors. Originality/value – Existing methods for inertial and magnetic sensor fusion are combined to achieve reliable estimation of yaw angle. Experimental tests in a human–robot interaction scenario show the performance of the system.

scholarly journals Development of a chopper charge amplifier for measuring the cavity pressure inside injection moulding tools and signal optimisation with a Kalman filter

2017 ◽  
Vol 6 (1) ◽  
pp. 199-210
Author(s):  
Manuel Schneider ◽  
Alexander Jahn ◽  
Norbert Greifzu ◽  
Norbert Fränzel
Keyword(s):  
Kalman Filter ◽  
Injection Moulding ◽  
Low Cost ◽  
Pressure Sensors ◽  
Cost Effective ◽  
Low Frequencies ◽  
Sensor Signals ◽  
Chopper Amplifier ◽  
Insight Into

Abstract. This article provides insight into the development of a powerful and low-cost chopper amplifier for piezoelectric pressure sensors and shows its possible applications for injection moulding machines. With a power supply of 3.3 volts and the use of standard components, a circuit is introduced which can be connected to a commercially available microcontroller without any additional effort. This amplifier is specialised for low frequencies and high-pressure environments. With the adjustment of the sample and chopper frequency by means of software, the amplifier can easily be adapted for other applications. This chopper amplifier is a very compact and cost-effective solution with a small number of required components. In this contribution, it will be shown that the amplifier has good results in various laboratory tests as well as in the production process. Furthermore, an approach to fuse data from force and pressure signals by using a Kalman filter will be presented. With this method, the quality of the sensor signals can be significantly improved. This article is an extension of our previous work in Schneider et al. (2016b).

Improving the accuracy of a vibratory sensor using Kalman filtering

Robotica ◽  
1993 ◽  
Vol 11 (2) ◽  
pp. 129-138 ◽  
Author(s):  
D.T. Pham ◽  
K. Hafeez
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Natural Frequencies ◽  
Industrial Robot ◽  
Sensor System ◽  
Location Information ◽  
Location Determination ◽  
Sensor Signals ◽  
Filtering Technique ◽  
Position And Orientation

SUMMARYThis paper presents a Kalman filtering technique for reducing errors in locating 3-D objects using a sensor system. The location information is employed to control the motion of an industrial robot to pick up the objects. The sensor consists of a rigid platform mounted on a flexible column. Each object to be located is placed on the sensor. The static deflections and natural frequencies of vibrations of the sensor are measured and processed to determine the position and orientation of the object. In practice, the sensor signals obtained are corrupted with noise leading to errors in location determination. A Kalman filter is used to reduce the noise present in the sensor system.

scholarly journals A reduction method for magnetic disturbance caused by DC railway system

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xuezhen Ding ◽  
Yuguo Li ◽  
Yunju Wu ◽  
Shuangmin Duan ◽  
Zhuoxuan Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Reduction Method ◽  
Filter Method ◽  
Magnetic Disturbance ◽  
Adaptive Kalman Filter ◽  
Railway Systems ◽  
Geomagnetic Data ◽  
Railway System ◽  
Kalman Filter Method ◽  
High Level

AbstractThe stray current of direct current (DC) railway systems causes magnetic disturbance in geomagnetic measurements, which may complicate the identification of useful information. The magnetic disturbance exhibits broadband characteristics in the frequency domain. In this paper, we propose a noise reduction method based on the adaptive Kalman filter to extract useful signals from the geomagnetic data with a high level of noise. The covariance matrixes of both the process noise (Q) and measurement noise (R) can be adaptively estimated to improve the performance of the adaptive Kalman filter. The proposed method is adopted to process the geomagnetic data collected at the Beijing Geomagnetic Observatory (BJI), which is affected by the DC railway system. The magnetic disturbance is largely reduced, and the signal-to-noise ratios of the horizontal and vertical components of the geomagnetic field are improved by more than 14 dB and 27 dB, respectively. The K-indices are calculated to evaluate the performance of the adaptive Kalman filter method. To assess the influence of the adaptive Kalman filter on natural signals, the geomagnetic data that contain rapid variations are processed. The denoising results show that the adaptive Kalman filter can effectively reduce the magnetic disturbance caused by DC railway system without large impact on the natural geomagnetic rapid variations.

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