Inertial localization system using unscented Kalman filter for 3D positioning (original) (raw)
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Sensors, 2018
In recent years, a variety of real-time applications benefit from services provided by localization systems due to the advent of sensing and communication technologies. Since the Global Navigation Satellite System (GNSS) enables localization only outside buildings, applications for indoor positioning and navigation use alternative technologies. Ultra Wide Band Signals (UWB), Wireless Local Area Network (WLAN), ultrasonic or infrared are common examples. However, these technologies suffer from fading and multipath effects caused by objects and materials in the building. In contrast, magnetic fields are able to pass through obstacles without significant propagation errors, i.e. in Non-Line of Sight Scenarios (NLoS). The aim of this work is to propose a novel indoor positioning system based on artificially generated magnetic fields in combination with Inertial Measurement Units (IMUs). In order to reach a better coverage, multiple coils are used as reference points. A basic algorithm for three-dimensional applications is demonstrated as well as evaluated in this article. The established system is then realized by a sensor fusion principle as well as a kinematic motion model on the basis of a Kalman filter. Furthermore, a pressure sensor is used in combination with an adaptive filtering method to reliably estimate the platform's altitude.
Journal of Sensor Technology, 2014
This Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer technologies have been widely used in a variety of positioning and navigation applications. In this paper, a low cost solid state INS/GPS/Magnetometer integrated navigation system has been developed that incorporates measurements from an Inertial Navigation System (INS), Global Positioning System (GPS) and fluxgate magnetometer (Mag.) to provide a reliable complete navigation solution at a high output rate. The body attitude estimates, especially the heading angle, are fundamental challenges in a navigation system. Therefore targeting accurate attitude estimation is considered a significant contribution to the overall navigation error. A better estimation of the body attitude estimates leads to more accurate position and velocity estimation. For that end, the aim of this research is to exploit the magnetometer and accelerometer data in the attitude estimation technique. In this paper, a Scaled Unscented Kalman Filter (SUKF) based on the quaternion concept is designed for the INS/GPS/Mag integrated navigation system under large attitude error conditions. Simulation and experimental results indicate a satisfactory performance of the newly developed model.
Using Magnetic Disturbances to Improve IMU-Based Position Estimation
European Control Conference, 2007
Abstract We address the problem of position estimation for a rigid body using an inertial measurement unit (IMU). In this paper, we present a Kalman filtering technique which takes ad-vantage of the magnetic disturbances usually observed indoors. This is an important topic for military ...
—This paper describes a method for spatial tracking of a strapdown device that can be used for design of human-computer interfaces. Inertial Measurement Unit (IMU) is used to obtain 6-dof position exploiting the so-called ZUPT technique by the means of the Kalman Filter. Additional corrections of position are done using magnetometer readings in the presence of static magnetic field induced by permanent magnet that overshadow geomagnetic field. This correction allows us to overcome drifting errors of integration of IMU readings. We have also presented comparisons of different models for magnetic field reconstruction that is crucial for this system.
IEEE Transactions on Industrial Electronics, 2000
This paper presents a novel methodology that estimates position and orientation using one position sensor and one inertial measurement unit. The proposed method estimates orientation using a particle filter and estimates position and velocity using a Kalman filter (KF). In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results show that the orientation errors using the proposed method are significantly reduced compared to the orientation errors obtained from an extended Kalman filter (EKF) approach. The improved orientation estimation using the proposed method leads to better position estimation accuracy. This paper studies the effects of the number of particles of the proposed filter and position sensor noise on the orientation accuracy. Furthermore, the experimental results show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown.
Implementing a Sensor Fusion Algorithm for 3D Orientation Detection with Inertial/Magnetic Sensors
In this paper a sensor fusion algorithm is developed and implemented for detecting orientation in three dimensions. Tri-axis MEMS inertial sensors and tri-axis magnetometer outputs are used as input of fusion system. A Kalman filter is designed to compensate the inertial sensors errors by combining accelerometer and gyroscope data. A tilt compensation unit is designed to calculate the heading of the system.
Design and Implementation of Inertial Navigation System
Inertial Navigation combined with other navigation supports like GPS, has gained importance due to enhanced navigation and inertial reference performance. The INS individually can compute the position of the device without any help from the outside world. However, a huge number of errors are introduced by sensors giving rise to an unacceptable drift in the output. So, a GPS is used to help the INS, using a Kalman filter which helps in estimating the errors in the INS and thus updating position to improved accuracy.
Improvement of the Proprioceptive-Sensors based EKF and IMM Localization
2008 11th International IEEE Conference on Intelligent Transportation Systems, 2008
This paper presents the localization problem of outdoor vehicles using Interacting Multiple Model (IMM) and Extended Kalman Filter (EKF), in their predictive step without exteroceptive sensors data. Usually, hybridization operates between exteroceptive sensors (e.g. GNSS 1) and proprioceptive sensors (e.g. Odometer, Inertial Measurement Unit etc.) through a merging algorithm. Common experiments use the GPS receiver PPS time for stamping the odometric, gyrometric and IMU measurements, after what all these sensors are in the same UTC reference time. Now it is well known that the low cost GNSS devices have a very low frequency compared to proprioceptive sensors, combined to a low accuracy. Therefore in order to assess the vehicle positioning at higher frequency for safety applications, the sensors measurements are generally synchronized before being exploited in the merging algorithm. In our approach, the sensors remain in their original frequencies. The objective is to design a reliable and robust system that exploits asynchronous data. In order to reach this goal it is important to guarantee accuracy and integrity of filters even during the predictive steps, when exteroceptive GNSS data are not available: that is proprioceptive-sensors based positioning. We introduce in this paper, a study on the influence of the road bank angle assessment on the output. This parameter is used to correct the gyrometric and inertial unit measurements leading to an improvement of both IMM and EKF predictive output positioning. Tests performed with real data proved the suitability of introducing this parameter in the system.
Statistical sensor fusion of a 9-DoF MEMS IMU for indoor navigation
Sensor fusion of a MEMS IMU with a magnetometer is a popular system design, because such 9-DoF (degrees of freedom) systems are capable of achieving drift-free 3D orientation tracking. However, these systems are often vulnerable to ambient magnetic distortions and lack useful position information; in the absence of external position aiding (e.g. satellite/ultra-wideband positioning systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates rapidly due to unmodelled errors. Positioning information is valuable in many satellite-denied geomatics applications (e.g. indoor navigation, location-based services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking method using batch optimization. By adopting a robust in-situ user self-calibration approach to model the systematic errors of the accelerometer, gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed least-squares framework, the accuracy of the estimated trajectory from a 9-DoF MEMS IMU can be improved. Through a combination of relative magnetic measurement updates and a robust weight function, the method is able to tolerate a high level of magnetic distortions. The proposed auto-calibration method was tested in-use under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket, a person checking their phone, and a person walking with a smartwatch. In these experiments, the presented algorithm improved the in-situ dead-reckoning orientation accuracy by 79.8 – 89.5% and the dead-reckoned positioning accuracy by 72.9 – 92.8%, thus reducing the relative positioning error from metre-level to decimetre-level after ten seconds of integration, without making assumptions about the user's dynamics.