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Inertial Navigation Systems with three 3D sensors are used to localize moving persons. The accuracy of the localization depends on the quality of the sensor data of the multi-sensor system. In order to improve the accuracy, a self-calibration process based on the automatic 3D calibration was developed. Based on the calibration procedure of the accelerometer (ACC) and the magnetic field sensor (MAG), the additional integration of the gyroscope (GYRO) leads to a reduction of the indoor positioning error. This improves both the approximation for the accelerometer and the magnetic field sensor so that the standard deviation of a single sensor is minimized. A new calibration procedure of the gyroscope and the accuracy improvement of the localization of a moving person are presented.
In this work an air-based sensor network for the localization of persons at extensive areas is presented. The developed network consists of a localization device which the person is wearing (BodyGuard-System), a mobile relay station in the air, and a base station. All three parts communicate with the same radio chip. The BodyGuard-System is an inertial navigation system which was developed for localization in difficult environments with high accuracy and low measurement uncertainty. To increase the range of the system, a multi-hop network was built up. The measured data of the BodyGuard-System and the mobile relay station is visualized on a PC in the base station. This multi-hop network is necessary for example for fire department missions.
Fiber optic sensors have gained increasing importance in recent years and are well established in many areas of industrial applications. In this paper, we introduce a concept of a self-diagnostic fiber optic sensor. The presented sensor is to resolve the problems of embedded fiber optic sensors in complex structures and to enable the validation under operational conditions. For this purpose, different magnetostrictive coated fiber optic sensors were developed and various experiments were performed to verify their mode of operation and to determine the respective reproducibility. The measuring principle is illustrated by obtained experimental results, which showed a change in wavelength from 1 pm at a magnetic field strength change of 0.25 mT. In addition, the temperature characteristics of the implemented magnetostrictive sensor were analyzed and an experimental factor of 1.5 compared to a reference fiber optic sensor was determined.
In den letzten Jahren ist die Bestimmung der Position eines sich bewegenden Objektes eine immer interessanter werdende Information geworden. Die aktuelle Position oder der zurückgelegte Weg wird mittels miniaturisierter integrierter 3D-Sensoren bestimmt. Durch die Integration dieser Sensoren in moderne Smartphones oder in spezielle robuste Hardware sind die Anwendungsgebiete vielseitig. Die ermittelte Position innerhalb von Gebäuden ist dabei für den Kaufmann innerhalb eines Shoppingcenters aber auch für den Feuerwehrmann im Einsatz wichtig. Für den Kaufmann bedeutet es durch gezielte Angebote potenzielle Kunden anzusprechen und damit seinen Umsatz zu steigern. Für den Einsatzleiter der Feuerwehr ist die genaue Position seiner Feuerwehrmänner im Einsatz lebensrettend und bietet damit eine zusätzliche Sicherheit für die Einsatzkraft. Das hier vorgestellte Multisensorsystem, allgemein als Inertialsystem bezeichnet, und das darauf angewendete Verfahren zur Positionsbestimmung wird in diesem Bericht vorgestellt und näher beschrieben. Die systematischen und zufälligen Messunsicherheitsbeiträge des Inertialsystems und deren Auswirkungen auf die Bestimmung der aktuellen Position und somit auf den resultierenden zurückgelegten Weg werden erörtert.
Location estimation and navigation, especially on smartphones has shown great progress in the past decade due to its low cost and ability to work without additional infrastructure. However, a challenge is the positioning, both in terms of step detection, step length approximation as well as heading estimation, which must be accurate and robust, even when the use of the device is varied in terms of placement or orientation. In this paper, we propose a scheme for retrieving relevant information to detect steps and to estimate the correct step length from raw inertial measurement unit (IMU) data. This approach uses Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs). Designed to take contextual information into account, the network can process data gathered from different positions, resulting in a system, which is invariant with respect to transformation and distortions of the input patterns. An experimental evaluation on a dataset produced from 10 individuals demonstrates that this new approach achieves significant improvements over previous attempts and increase the current state-of-the-art results even in the presence of variations and degradations. We achieved a mean classification rate of 98.5% and a standard deviation of 0.70 for 10000 different test sequences and an average error of 1.45% regarding the step length. Thus is the best result on the task gathered in the experiments compared with competing techniques.
An auto-validation tool for the reliability quantification of materials integrated fiber Bragg grating (FBG) strain sensors have been developed and tested. The FBG strain sensor was jacketed with a magnetostrictive layer based on iron-nickel which, when excited by a specific magnetic field, adds an artificial strain to the sensor. The fixed relationship between magnetic induction and wavelength shift of the FBG strain sensor characterizes the bond strength and adhesion between the sensor and the surrounding structure. Due to an easily applicable magnetic field, it is possible to validate the sensor performance in a non-contact, fast way without disturbing the data-acquisition process.
Structural health monitoring contributes to early damage detection in the built infrastructure. During the last two decades, sensor networks transferred from wired to wireless sensor networks. Several methods exist to measure moisture in building materials. Most of the introduced commercial moisture measurement methods provide information about the local or near surface moisture. A feasibility study is presented to demonstrate, how the Received Signal Strength Indicator (RSSI) of a Bluetooth Low Energy (BLE) signal, transmitted from the BLE-module embedded in building materials, correlate with changing moisture content. The feasibility of this influence was investigated in the presented study. The maximum and minimum RSSI were systematically recorded and analysed.