Guenther Retscher

Active RFID Trilateration and Location Fingerprinting Based on RSSI for Pedestrian Navigation

In the work package ‘Integrated Positioning’ of the Ubiquitous Cartography for Pedestrian Navigation project (UCPNAVI) alternative location methods using active Radio Frequency Identification (RFID) are investigated for positioning of pedestrians in areas where no GNSS position determination is possible due to obstruction of the satellite signals. In most common RFID applications, positioning is performed using cell-based positioning. RFID tags can be installed at active landmarks (i.e., known locations) in the surroundings and a user equipped with an RFID reader can be positioned using Cell of Origin (CoO). The positioning accuracy, however, depends on the size of the cell defined by the maximum range of the signal. Using long range RFID for positioning the cell size can be quite large, i.e., around 20 m. Therefore, the paper proposes two new methods for positioning, i.e., trilateration and location fingerprinting based on received signal strength indication (RSSI) if more than one RFID tag is visible. The trilateration approach is based on the deduction of ranges to the RFID tags from RSSI. An iterative approach to model the signal propagation will be introduced, i.e., the International Telecommunication Union (ITU) indoor location model that can be simplified to a logarithmic model, and a simple polynomial model is employed for the signal strength to range conversion. In a second attempt, location fingerprinting based on RSSI is investigated. In this case, RSSI is measured in a training phase at known locations inside the building and stored in a database. In the positioning phase these measurements are used together with the current measurements to obtain the current location of the user. For the estimation of the current location different approaches are employed and tested, i.e., a direction-based approach, a tag-based approach, a direction-tag-based approach and a heading-based approach. Using trilateration or fingerprinting positioning accuracies on the one to a few metres level can usually be achieved. The concept and the iterative approach of the different methods and test results are discussed in this paper.

Continuous indoor navigation with RFID and INS

Some navigation applications, such as the navigation of blind users, require that a continuous positioning is performed in combined outdoor urban and indoor environments with a certain positioning accuracy. For outdoor urban environments usually GNSS and dead reckoning are employed and has proven to be satisfactory. In indoor environments, however, absolute position determination with an accuracy in the range of 1 to 2 meters is still very challenging. For indoor location determination, a number of different positioning methods have been developed. In our approach, Radio Frequency Identification (RFID) has been selected and investigated. Using RFID most commonly cell-based positioning is performed. Apart from that we have also investigated trilateration and location fingerprinting based on signal strength measurements (i.e., RSSI short for Received Signal Strength Indication) from the RFID tags in the surrounding environment. The disadvantage of these two positioning methods, however, is the required calibration in the off-line or training phase to deduce ranges to the tags from the RSSI measurements in the case of trilateration or the establishment of the RSSI database at known locations throughout the building in the case of location fingerprinting. Therefore we have developed a new location method based on cell-based positioning which makes use of the measured RSSI in the on-line or positioning phase, i.e., the so-called time-based Cell-of-Origin (CoO). Two modifications have been implemented in comparison to common CoO and will be discussed in the paper. The new approach was then be tested in an indoor environment in an office building of the Vienna University of Technology. It could be seen that for a combined positioning of RFID time-based CoO and a low-cost MEMS-based INS positioning accuracies on the 1 to 2 meter level can be achieved. The different experiments performed in the test bed are described and discussed in this contribution.

Investigation of location capabilities of four different smartphones for LBS navigation applications

The market of smartphones and other mobile devices shows very high increase rates nowadays, e.g. the Apples iOS-based smartphone and tablet series have gained 15% of global market share, while the Android-based smartphones and tablets have a share of 52.5% [11]. The intelligence contained in smartphones relies very much on the integration of different low-cost and compact sensors. MEMS-based accelerometers and magnetometers (or digital compasses) are integrated allowing manifold scenario-awareness applications (apps). Apple began this revolution by equipping the iPhone with an accelerometer to switch its display automatically from portrait to landscape orientation. Now Apple has a storeful of novel apps that exploit the iPhones accelerometer for gaming, health monitoring, sports training and countless other uses thought up by legions of developers. It is forecasted that 85% of all smartphones by 2013 will include GPS, over 50% will have accelerometers and almost 50% will have gyroscopes [6]. Using these sensors smartphones offer location and navigation functionalities. Accelerometers can be used to determine the current movement state of the user, e.g. standing, walking, or fast moving in a car or public transportation. In addition, the digital compass can provide the orientation of movement. The research study discussed in this paper investigates the use of GPS and other geosensors for navigation applications. The conducted field tests cover combined indoor/outdoor environments in urban areas in the city of Vienna, Austria. In the tests the navigation capabilities of four different smartphones are investigated, namely an Apple iPhone 4, a Samsung Galaxy SII, a HTC EVO 3D and a Nokia X7. One main objective of the presented tests is to assess the quality of the data provided by the sensors in these smartphones. The test results show positioning accuracies on the few meter level using either GPS or dead reckoned positioning solutions with calibrated accelerometer and compass measurements. Therefore the feasibility of using smartphones for positioning in LBS and other navigation applications could be proven.

Initial test on the use of GPS and sensor data of modern smartphones for vehicle tracking in dense high rise environments

Modern smartphones normally incoporate a high sensitivity GPS receiver, Wi-Fi card, and sensors such as accelerometer, digital compass and digital barometer. These components are low-cost, and are designed mainly for leisure and gaming applications. This study aims to investigate the combination of the built-in GPS and sensor data of smartphones for localization in dense urban environments, where very often satellite signals are obstructed by tall buildings or large structures, causing insufficient number of GNSS measurement data for successful position determination. This paper is the continuation of our investigations on the characteristics of data outputs from a digital compass and accelerometer in relation to the orientation and phone movements presented in Mok et al. (2011) [7]. In the following, we will focus our discussions on the integration of GPS, digital compass, and accelerometer for vehicle tracking applications. Our investigation results show that the distance and orientation data derived from the outputs of the accelerometer and digital compass is generally sufficient to provide the shape of the path that the vehicle has travelled, with a varying scalar error. Magnetic to grid north correction, however, is necessary to improve the heading. By reducing the data sampling period from 30 seconds to 1 second, the scalar error can be significantly reduced. Moreover, correction for the gravity effect on the x-, y- and z-axes of the smartphones local coordinate system is the key to correct the determination of accelerometer-derived distance travelled.

Evaluating the performance of low cost MEMS inertial sensors for seamless indoor/outdoor navigation

For all mobile, location based applications, location availability (either on demand or continuously) is the primary performance requirement of the positioning technologies used. In most cases, this requirement outweighs that of meeting a specified accuracy, as the granularity of information provided to the user can be scaled around the computed positioning accuracy. What is therefore important is being able to generate a position solution and its accuracy at a specified level of confidence. For these applications, meeting the requirement of 100% availability is a significant challenge for individual positioning technologies, even more so when navigating between indoor and outdoor environments. Whilst operating under ideal operating conditions, GPS provides excellent positioning coverage. In indoor environments, position solutions can be generated using infrastructure based technologies such as RFiD and WiFi or augmentation sensors such as inertial navigation systems. Micro- Electromechanical Sensor (MEMS) inertial sensors are a popular option as they offer an autonomous capability that can potentially augment performance seamlessly across indoor and outdoor environments with marginal cost implications. This paper presents the results of a practical test undertaken to evaluate the performance of commercially available MEMS inertial sensors. In particular, results obtained that characterize the performance of these sensors against GPS in the transition zone between indoor and outdoor environments will be presented.

A new method for improving Wi-Fi-based indoor positioning accuracy

Wi-Fi- and smartphone-based positioning technologies are playing a more and more important role in location-based service industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the indoor positioning accuracy was developed. The new method initially used the nearest neighbour (NN) algorithm of the fingerprinting method to identify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the path loss model based on the two signal strength indicator values observed. The systematic error from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.

Collaborative navigation as a solution for PNT applications in GNSS challenged environments: report on field trials of a joint FIG / IAG working group

Abstract PNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10–15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques – so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled ‘Ubiquitous Positioning Systems’ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ‘network’ or ‘neighbourhood’ of users is to be positioned / navigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users.

A case study on the feasibility and performance of an UWB-AoA real time location system for resources management of civil construction projects

Abstract The application of integrated satellite and modern wireless positioning technologies for ubiquitous real-time resources management in large scale civil engineering projects can greatly optimize the time and cost in the construction process, and is now the trend for modern construction project management. As the outdoor conditions of most civil construction sites are open to sky, satellite positioning with the popularly used Global Positioning System (GPS) has been proved to be very efficient and effective. However, the condition in indoor and underground construction site is very complicated due to the fact that different construction activities would be carried out in different congested areas, involving heavy construction plant, equipment, professionals and technical personnel. Nowadays different emerging technologies such as Wi-Fi and ZigBee can be adopted for position and tracking in indoor environments. Nevertheless, under the very complicated construction site conditions these technologies may fail due to movement of human resources and construction plant, variation of metrological conditions, and serious multipath effects of signals. It is considered that Ultra Wide Band (UWB) technology is more suitable for indoor construction site environments. In this paper, a case study on the attempt of integrating GPS with Ubisense Real-time Location System (RTLS) for resources management in an underground railway construction site is discussed. Laboratory and field tests have shown that the RTLS can provide better resources management capability in terms of positioning accuracy and stability than Wi-Fi and ZigBee technologies under complicated construction environments. The test results show that the system can normally achieve better than 15 cm accuracy, and better than 1 m under adverse geometrical site condition. However, the high instrumental set up cost and the requirement for high quality data transmission cable for high precision time synchronization between sensors may deter wide application of similar system for resources management in construction sites.

Wi-Fi Fingerprinting with Reduced Signal Strength Observations from Long-Time Measurements

Indoor positioning which uses signal strength values of Wi-Fi networks have become popular as these wireless networks often already exist and many mobile devices, such as smartphones or tablets, have built-in Wi-Fi cards. Usually fingerprinting is employed for positioning which achieves relatively low positioning accuracies on the several meter level. In the scope of this work two methods are presented which have the potential to improve the fingerprinting performance using long-time RSS observations at reference stations. Both methods employ the usage of at least three reference stations surrounding the area of interest on which signal strength observations are continuously performed during the whole measurement process. Thereby the first method uses a 2-D linear plane-interpolation for the deduction of real-time corrections. For that purpose, the measured signal strengths are reduced by the long-time measurements which are interpolated at the approximate position of the measuring point. In the second method the daily average of the long-time measurements is applied and the improvements of the measurements are calculated by the deviation from the daily average. For this method it is conceivable that a single reference station may be sufficient if it is located in the middle of the area of interest. Field tests were performed in an office building and are analyzed. The fingerprinting algorithms reached an averaged positioning accuracy of around 5 m in dependence on the used smartphone. The daily average improvements (DAI) method provided a better performance than the interpolation method which is highly influenced by the required approximate position of the user.

Cooperative Localization of Unmanned Aerial Vehicles Using GNSS, MEMS Inertial, and UWB Sensors

AbstractCooperative networks of low-cost unmanned aerial vehicles (UAVs) are attracting researchers because of their potential to enhance UAV performance. Cooperative networks can be used in many a...