Robuste und sicherheitsrelevante Echtzeitlokalisation (RosiE)

Laufzeit: 01.06.2016 - 31.10.2018
Leitung: Prof. Dr.-Ing Horst Hellbrück
Mitarbeiter: Manfred Constapel (Hauptverantwortlicher),Tim EsemannMarco CimdinsMathias Pelka

Hintergrund

Für Anwendungen, in denen Industrieroboter autonom agieren und/oder autonome Fahrzeuge fahren, ist das Gefährdungspotential für Menschen sehr hoch. Zur Überwachung eines Sicherheitsbereichs zum Schutz von Mensch und Technik ist der Einsatz von Echtzeitlokalisation vorteilhaft. Ist eine Gefährdung wie der Aufenthalt von Menschen in einer Sperrzone erkannt, sind umgehend Gegenmaßnahmen wie z.B. Alarm, Voll- oder Teilabschaltung der Maschinen einzuleiten. Dazu sind die Lokalisationsinformationen an ein übergeordnetes Systems weiterzuleiten. 

Ziel

Ziel des Forschungs- und Entwicklungsvorhabens ist die Entwicklung und prototypische Umsetzung einer robusten und sicherheitsrelevanten Echtzeitlokalisation auf Basis von Funksignalen. Dazu  werden neuartige Lokalisationstechniken benötigt und entwickelt. Das eingesetzte Verfahren soll in der Lage sein, Objekte im Sicherheitsbereich zu verfolgen und den Eintritt in einen Sperrbereich zu erkennen und zu melden. Zur Zutrittskontrolle in den Sicherheitsbereich wird ein passives Lokalisationssystem verwendet, während für den Sicherheits- und Sperrbereich eine Anker /Tag basierte Lokalisierung verwendet wird. 

Arbeiten im Rahmen des RosiE Projektes

Es ergeben sich ständig neue Aufgaben im Rahmen des Forschungsprojektes. Sollten Sie eigene Ideen haben, die sich im Rahmen dieses Projektes verwirklichen wollen, schreiben Sie uns einfach an

Wir suchen zur Zeit Studenten für folgende Arbeiten:

Zur Zeit laufende Untersuchungen:

  • keine!

Abgeschlossene Untersuchungen:

Zwischenergebnisse der aktiven Ortung

Um Ortungssysteme robuster zu gestalten wurden verschiedene Ansätze sowie Ortungstechnologien untersucht. So wurden zum Beispiel Bluetooth-basierte Ortungsmethoden auf Basis von Lateration, Fingerprinting sowie Proximity untersucht. Ebenfalls wurden Funktechnologien wie z.B. LoRa, Bluetooth sowie IEEE 802.15.4 (ZigBee) auf ihre Robustheit untersucht. 

Um die Robustheit des Ortungssystems zu erhöhen, ist es eine möglich die Anzahl der benötigten Referenzpunkte zu reduzieren. Durch die Verwendung nur eines Referenzpunktes, ist weniger Infrastruktur notwendig. Dies senkt die Konsten für den Aufbau von Ortungssystemen. Für eine normale Gebäudegeometrie, wie im gezeigten Beispiel wurde untersucht, ob Ortung mit nur einem Referenzpunkt möglich ist.

Ein Ansatz ist es, mehrere Antennen in einen Referenzpunkt zu integrieren, um bewährte Technologien wie (hyperbolische) Lateration auf Basis von hochpräzisen Zeitmessungen bzw. Distanzmessungen zu nutzen. Dies stellt hohe Anforderungen an die verwendete Hardware, da z.B. die Messabweichungen der Distanzmessungen in der Größenordnung der verwendeten Antennengeometrie liegen. 

Auch durch geeignete Filter, wird die Robustheit des Ortungssystems gegenüber Signalstörungen wie z.B. Abschattungen oder Nicht-Sichtverbindungen erhöht. So werden Messabweichungen durch eine Kombination aus linearen und nichtlinearen Filtern korrigiert. Durch nachgeschaltete Filter, welche die Bewegung eines Objektes vorhersagen, wird im Falle eines Ausfalls des Ortungssystems die Position einer Person dennoch bestimmt. 

Robuste Ortung auf Basis von Distanzmessungen

In einer weiteren Untersuchung wurden fünf unterschiedliche Arten der Zwei-Wege Distanzmessung untersucht und bewertet. Diese weisen unterschiedliche Eigenschaften, wie z.B. Anzahl der Nachrichten, Einfluss des Uhrenfehlers und Robustheit auf. In der Untersuchung wurde zusätzlich geklärt, wie genau die Distanzmessung der Verfahren ist. Zusätzlich wurde untersucht, ob Unterschiede zwischen Freifeld-Bedingungen oder Innenraum-Bedingungen vorliegen. Das folgende Bild zeigt beispielsweise den systematischen Fehler für den euklidischen Abstand zwischen wahrer Distanz und gemessener Distanz in Abhängigkeit von der wahren Distanz. 

Dieser systematische Fehler wird durch zwei Effekte verursacht: Zum einen handelt es sich um den Uhrenfehler, hervorgerufen durch Abweichungen in den Oszillatoren. Zum anderen dadurch, dass der Funkempfänger eine Abhängigkeit von der Signalempfangsstärke aufweist. 

Zwischenergebnisse der gerätefreien Ortung

Das Ziel von gerätefreien Ortungssystemen ist die Erkennung der Präsenz und die anschließende Ortung von Personen ohne speziell mitgeführte Geräte. Bewegt sich eine Person in einem Zielbereich, so entstehen durch Ausbreitungseffekte Änderungen der Signalstärke eines Funksignals. Dazu wird in einer Trainingsphase, wenn der Zielbereich frei ist, die Signalstärken gemessen und ein Schwellwert bestimmt.

Prinzip des gerätefreien Ortungssystems

Wird in der Onlinephase dieser Schwellwert überschritten, so wurde eine Person erkannt, welche anschließend mit nichtlinearen Filtern geortet wird. 

In der folgenden Abbildung ist ein Flur zu sehen, welcher mit 8 Sensoren (dargestellt durch die roten Punkte) gerätefrei überwacht wird. Der blaue Punkt stellt die tatsächliche Position, das schwarze Kreuz die durch den Filter geschätzte Position der Person dar. Die grauen Kreise, die sog. Partikel, sind Zwischenberechnungen/Stichproben für die Positionsschätzung. Idealerweise folgen die Partikel den Bewegungen der Person und ermöglichen somit eine genaue Ortung.

Ein weiterer essentieller Schritt für die Entwicklung von System ist das Verständnis der Funkausbreitung in An- und Abwesenheit einer Person. Hierzu wird der Einfluss der Person mittels Kantenbeugung modelliert und gegen die Freifeldsignalstärke verrechnet. Bewegt sich eine Person durch die Mitte einer Funkübertragung ergeben sich folgende Messungen:

Die blaue Kurve sind Messwerte, die gelbe Kurve die Simulation anhand des Modells.

 

Projektpartner

 

files/mflo/BMWi_Web_de_WBZ.gif

Förderkennzeichen: ZF4186102ED6

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Veröffentlichungen


Artikel and Buchkapitel
[2018] A new localization algorithm based on neural networks (Mathias Pelka, Manfred Constapel, Duc Tu Le Anh, Horst Hellbrück), In Proceedings of the 3rd KuVS/GI Expert Talk on Localization, 2018. [bib] [pdf] [abstract]
Indoor localization plays a major role in a wide range of applications. To determine the location of a tag, localization algorithm is required. In the past, machine learning algorithms were difficult to implement in consumer hardware, but with the advent of tensor processing units, even smartphones are capable to use artificial intelligence to solve complex problems. In this paper, we investigate a machine learning algorithm based on neural networks and compare the result to a linear least squares estimator. We design and evaluate different neural networks. Based on our observation, the neural network delivers poor performance compared to the linear least squares estimator.
[2017] UWB-based Single Reference Point Positioning System (Mathias Pelka, Swen Leugner, Marco Cimdins, Holger Schwegmann, Horst Hellbrück), In ITG-Fachbericht-Mobilkommunikation VDE VERLAG GmbH, 2017. [bib] [abstract]
Indoor positioning enables new applications, for instance monitoring of goods in smart factories. For such applications, indoor positioning requires cost-effective solutions with high accuracy. State-of-the-art positioning systems are expensive due to high infrastructure and maintenance costs. In this paper we suggest an accurate UWB-based single reference point positioning system using multiple antennas. We compare lateration and hyperbolic lateration as positioning methods and present efficient algorithms for UWB-based single reference point positioning systems. We present theoretical limits based on the Cramer-Rao lower bound and derive an error estimation as well as evaluation results. Our measurements indicate that decimeter accuracy is possible.
Konferenz Beiträge
[2018] Minimizing Indoor Localization Errors for Non-Line-of-Sight Propagation (Mathias Pelka, Peter Bartmann, Swen Leugner, Horst Hellbrück), In International Conference on Localization and GNSS, 2018. [bib] [abstract]
Indoor Localization becomes more important, as it provides additional context for many applications for example in the Internet of Things (IoT). Time-of-flight measurements, as a basis for distance estimation, are susceptible for non-line-of-sight (NLOS) propagation, resulting in large distance errors. Standard least squares solutions to estimate the targets location do not account for NLOS propagation which results in large scale errors. We investigate the difference between L1- and L2-minimization and present a new framework based on a modified RANSAC approach. Additionally, we investigate a Support Vector Machine (SVM) to detect NLOS measurements.We present simulation and measurement results and evaluate our approach. We show that our framework delivers better performance in presence of NLOS propagation compared to plain L1- or L2-minimization.
[2018] RF-Based Safety-Critical Hybrid Localization (Mathias Pelka, Marco Cimdins, Horst Hellbrück), In The Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018. [bib] [abstract]
In safety-critical environments, e.g. paint or machine shops, precise knowledge of positions of persons is important. If an emergency is detected, e.g. a fire or an intruder in the safety-critical area of heavy machinery, emergency shutdown procedures are activated. This requires fine-grained localization with a tag-based localization system, where the person carries a tag. Without a person carrying a tag, precise localization and detection is difficult. In this paper, we propose a RF-based hybrid localization system for safety-critical localization that consists of two major components: a tag-based and device-free subsystem. The device-free subsystem provides coarse-grained localization and monitors a gate area, serving as an entrance towards the safety-critical area where fine-grained localization is required. We propose an architecture of the system, discuss our setup and evaluate typical use cases. Our preliminary evaluation demonstrates that our system detects the correct state of the hybrid localization system with an accuracy of 90%.
[2018] Sundew: Design and Evaluation of a Model-based Device-free Localization System (Marco Cimdins, Mathias Pelka, Horst Hellbrück), In The Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2018. [bib] [abstract]
The state-of-the-art in device-free localization systems based on RF-measurements is fingerprinting. Fingerprinting requires reference measurements called fingerprints that are recorded during a training phase. Especially in device-free localization systems, recording of reference measurements for fingerprinting is a tedious, costly, and error-prone task. In this paper, we propose Sundew, a model-based device-free localization system that does not need fingerprinting in the sense of reference measurements but is able to calculate signal strength values at any position and compare it to actual measurements after a simple calibration phase. Sundew — as any device-free localization system — requires a metric for comparison of feature vectors. In this paper, we investigate the influence of nine different distance metrics on the positioning accuracy. Simulations and measurements show that our suggested model-based device-free localization system works best with the L 1 distance metric. Sundew estimates 90% of positions in a 2.5m x 2.5m grid correctly, independent of the orientation of the person in the target area.
[2016] Introduction, Discussion and Evaluation of Recursive Bayesian Filters for Linear and Nonlinear Filtering Problems in Indoor Localization (Mathias Pelka, Horst Hellbrück), In The Seventh International Conference on Indoor Positioning and Indoor Navigation, 2016. [bib] [abstract]
Linear and nonlinear filtering for state estimation (e.g. position estimation or sensor fusion) for indoor positioning and navigation applications is a challenging task. Sensor fusion becomes more important with cost-effective sensors being readily available. However, state estimation with recursive Bayesian filters for sensor fusion and filtering are difficult to apply. We present an overview for the general Bayesian filter and derive the most commonly used recursive Bayesian filters, namely the Kalman, extended Kalman and the unscented Kalman filter along with the particle filter. The later Kalman filters are extension of the original Kalman filter, which are able to solve nonlinear filtering problems. The particle filter is also able to solve nonlinear filtering problems. We evaluate the recursive Bayesian filters for linear and nonlinear filtering problems for sensor fusion from relative dead reckoning positioning data and absolute positioning data from an UWB positioning system. We discuss and evaluate performance and computational complexity and provide recommendations for the use case of the recursive Bayesian filters.
[2016] Investigation of Anomaly-based Passive Localization with Received Signal Strength for IEEE 802.15.4 (Marco Cimdins, Mathias Pelka, Horst Hellbrück), In The Seventh International Conference on Indoor Positioning and Indoor Navigation, 2016. [bib] [abstract]
Localization has important applications, for instance intrusion detection and elderly care. Such applications benefit from Device-free passive (DfP) localization systems, which employ received signal strength measurements (RSSM) to detect and track entities that neither participate actively in the localization process nor emit signals actively. RSSMs include received signal strength indicator (RSSI), energy detection (ED) and link quality indicator (LQI) measurements. This paper compares different packet-based RSSMs for DfP localization and presents detection results of a DfP anomaly-based detection system employed by IEEE 802.15.4 compliant devices. Furthermore, we investigate techniques for anomaly detection with continuous RSSI measurements.
Workshop Beiträge
[2017] Evaluation of time-based ranging methods: Does the choice matter? (Mathias Pelka, Daniel Amann, Marco Cimdins, Horst Hellbrück), In 14th Workshop on Positioning, Navigation and Communication, 2017. [bib] [abstract]
Positioning is useful in a number of applications, for instance smart home, smart factory and health care applications. Time-based ranging methods for positioning are the state-of-the-art but require precise timestamping. Sophisticated ranging methods compensate sources of errors, for instance clock drift caused by a crystal or an asymmetrical measuring principle, to provide precise timestamping. So far, no comprehensive study of different time-based ranging methods using the same hardware and the same evaluation setup was carried out. Consequently, we discuss, implement and evaluate five time-based ranging methods, including Two-Way Ranging, Double Two-Way Ranging, Asymmetrical Double-Sided Two-Way Ranging, Symmetrical Double-Sided Two-Way Ranging and Burst Mode Symmetric Double-Sided Two-Way Ranging. We evaluate accuracy, precision, robustness and run time for the ranging methods and answer the question if the choice of the time-based ranging method matters.
[2017] Anomaly-based Device-free Localization with Particle Filtering (Marco Cimdins, Mathias Pelka, Horst Hellbrück), In Workshop on Dependable Wireless Communications and Localization for the IoT, 2017. [bib] [abstract]
In the Internet of Things (IoT), devices, e.g. sensors or actuators, transmit packets to transfer data. For the IoT localization information is crucial, as it provides additional context for the data. We envision that devices in the IoT know their position and on receipt of a packet, the received signal strength is measured. This measurement is used to build a device-free localization (DFL) system to improve the dependability of the IoT system. DFL systems are able to detect and track persons within a target area that neither wear a device nor participate actively in the process of localization. This work presents an anomaly-based DFL system that measures if a person affects the radio frequency (RF) propagation and determines the position with a particle filter. In our 65m 2 indoor testbed, we employ eight IEEE 802.15.4 compliant wireless transceivers and estimate the position of a person with a median localization error of 1.4m.
[2017] Modeling Received Signal Strength and Multipath Propagation Effects of Moving Persons (Marco Cimdins, Horst Hellbrück), In 14th Workshop on Positioning, Navigation and Communication, 2017. [bib] [abstract]
Device-free localization (DFL) systems detect and track persons without devices that participate in the localization process. A person moving within a target area affects the electromagnetic field that is measured by received signal strength (RSS) values. Consequently for DFL systems modeling of RSS is important and still an open issue. In this paper, we develop a simple model for prediction of RSS values in a setup with transmitter and receiver devices, a person and multipath propagation. We design and implement the model as a superposition of both, knife-edge diffraction to account for the change made by the person, and, propagation effects such as multipath propagation that result in reflection and path loss including the antenna characteristics. We evaluate our model in comparison with real measurements in various setups with and without multipath propagation. We achieve an accuracy that is close to our hardware limitations, which is the resolution of the measured RSS values of the receiver.
Sonstige Veröffentlichungen
[2018] Impact of the antenna orientation for distance estimation (Mathias Pelka, Marco Cimdins, Horst Hellbrück), Technical report, Technische Universität Braunschweig, 2018. [bib] [pdf] [abstract]
Indoor localization is important for a wide range of use cases including industrial, medical and scientific applications. The location accuracy is affected by the localization algorithm and the quality of the measurements as input for the algorithm. Many indoor localization systems employ ultra-wideband distance measurements, as they offer high accuracy and are cost effective. One of the methods for distance measurement is twoway ranging. This paper investigates the impact of the antenna orientation on the distance measurement based on symmetrical double-sided two-way ranging. We show that up to 0.25m of the measurement error is attributed to the orientation of the antennas. We provide explanations and suggest solutions to reduce the effect.
[2018] Comparison of Antenna Types and Frequency Bands for Radio-based Device-free Localization (Marco Cimdins, Horst Hellbrück), Technical report, Technische Universität Braunschweig, 2018. [bib] [pdf] [abstract]
Radio-based device-free localization systems measure effects on radio signals e.g. signal strength variations to locate objects or persons in a target area. Such systems detect and track persons that do not participate in the localization process. Models for calculating the radio signal propagation are key for the performance in device-free localization systems. Received signal strength (RSS) is simple to measure. However, it is susceptible to changes in the environment and multipath propagation. In this paper, we compare PCB antennas to a circularly polarized cloverleaf antenna and measurements in the 2.4 GHz with measurements to the 868MHz ISM band. We investigate especially if a circularly polarized cloverleaf antenna is resilient against multipath propagation. Our preliminary results demonstrate that our model is suitable to the 868MHz band and the use of the 868MHz band increases the area where a person affects the RSS. The use of a circularly polarized cloverleaf antenna does not help to avoid multipath propagation.
[2017] Evaluation of Bluetooth Positioning for Medical Device Tracking (T. Kirchmann, M. Pelka, H. Hellbrück), GRIN (T. M. Buzug et. al., ed.), 2017. [bib]
[2016] Investigation of Anomaly-based Passive Localization with IEEE 802.15.4 (Marco Cimdins, Mathias Pelka, Horst Hellbrück), Technical report, RWTH Aachen University, 2016. [bib] [pdf]
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