Many applications in the area of location-based services and personal navigation require nowadays the location determination of a user not only in an outdoor environment but also an indoor. Typical applications of location-based services (LBS) mainly in outdoor environments are fleet management, travel aids, location identification, emergency services and vehicle navigation. LBS applications can be further extended if reliable and reasonably accurate three-dimensional positional information of a mobile device can be determined seamlessly in both indoor and outdoor environments. Current geolocation methods for LBS may be classified as GNSS-based, cellular network-based or their combinations. GNSS-based methods rely very much on the satellite visibility and the receiver-satellite geometry. This can be very problematic in dense high-rise urban environments and when transferring to an indoor environment. Especially, in cities with many high-rise buildings, the urban canyon will greatly affect the reception of the GNSS signals. Moreover, positioning in the indoor/outdoor transition areas would experience signal quality and signal reception problems, if GNSS systems alone are employed. The authors have proposed the integration of GNSS with wireless positioning techniques such as WiFi and UWB. In the case of WiFi positioning, the so-called fingerprinting method based on WiFi signal strength observations is usually employed. In this article, the underlying technology is briefly reviewed, followed by an investigation of two WiFi-positioning systems. Testing of the system is performed in two localisation test beds, one at the Vienna University of Technology and another one at the Hong Kong Polytechnic University. The first test showed that the trajectory of a moving user could be obtained with a standard deviation of about AE3-5 m. The main disadvantage of WiFi fingerprinting, however, is the required time consuming and costly signal strength system calibration in the beginning. Therefore, the authors have investigated if the measured signal strength values can be converted to the corresponding range to the access point. A new approach for this conversion is presented and analysed in typical test scenarios.

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.

In the work package '' Integrated Positioning '' of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environments) we are dealing with the navigation and guidance of visitors to our University. The start points are the public transport stops in the surroundings of the Vienna University of Technology and the system users should be guided to certain office rooms or persons. For the user's position determination different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors, such as a digital compass and gyro for heading determination, accelerometers for the determination of the distance travelled, a barometric pressure sensor for altitude determination and, for indoor areas, location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter. An optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the senors in the stochastic filter model, the sensor characteristics and performance were investigated in several tests. The tests were performed in different environments either with free satellite visibility, in urban canyons or inside buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach are presented in the paper. K E Y

For Wi-Fi positioning usually location fingerprinting or (tri)lateration are employed whereby the received signal strengths (RSSs) of the surrounding Wi-Fi Access Points (APs) are scanned on the mobile devices and used to perform localization. Within the scope of this study, the position of a mobile user is determined on the basis of lateration. Two new differential approaches are developed and compared to two common models, i.e., the one-slope and multi-wall model, for the conversion of the measured RSS of the Wi-Fi signals into ranges. The two novel methods are termed DWi-Fi as they are derived either from the well-known DGPS or VLBI positioning principles. They make use of a network of reference stations deployed in the area of interest. From continuous RSS observations on these reference stations correction parameters are derived and applied by the user in real-time. This approach leads to a reduced influence of temporal and spatial variations and various propagation effects on the positioning result. In practical use cases conducted in a multi-storey office building with three different smartphones, it is proven that the two DWi-Fi approaches outperform the common models as static positioning yielded to position errors of about 5 m in average under good spatial conditions.

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included.