Positioning algorithms for RFID-based multi-sensor indoor/outdoor positioning techniques

Position information has been very important. People need this information almost everywhere all the time. However, it is a challenging task to provide precise positions indoor/outdoor seamlessly. Outdoor positioning has been widely studied and accurate positions can usually be achieved by well developed GPS techniques. However, these techniques are difficult to be used indoor since GPS signals are too weak to be received. The alternative techniques, such as inertial sensors and radio-based pseudolites, can be used for indoor positioning but have limitations. For example, the inertial sensors suffer from drifting problems caused by the accumulating errors of measured acceleration and velocity and the radio-based techniques are prone to the obstructions and multipath effects of the transmitted signals. It is therefore necessary to develop improved methods for minimising the limitations of the current indoor positioning techniques and providing an adequately precise solution of the indoor positioning and seamless indoor/outdoor positioning. The main objectives of this research are to investigate and develop algorithms for the low-cost and portable indoor personal positioning system using Radio Frequency Identification (RFID) based multi-sensor techniques, such as integrating with Micro-Electro-Mechanical Systems (MEMS) Inertial Navigation System (INS) and/or GPS. A RFID probabilistic Cell of Origin (CoO) algorithm is developed, which is superior to the conventional CoO positioning algorithm in its positioning accuracy and continuity. Integration algorithms are also developed for RFID-based multi-sensor positioning techniques, which can provide metre-level positioning accuracy for dynamic personal positioning indoors. In addition, indoor/outdoor seamless positioning algorithms are investigated based on the iterated Reduced Sigma Point Kalman Filter (RSPKF) for RFID/MEMS INS/low-cost GPS integrated technique, which can provide metre-level positioning accuracy for personal positioning. 3-D GIS assisted personal positioning algorithms are also developed, including the map matching algorithm based on the probabilistic maps for personal positioning and the Site Specific (SISP) propagation model for efficiently generating the RFID signal strength distributions in location fingerprinting algorithms. Both static and dynamic indoor positioning experiments have been conducted using the RFID and RFID/MEMS INS integrated techniques. Metre-level positioning accuracy is achieved (e.g. 3.5m in rooms and 1.5m in stairways for static position, 4m for dynamic positioning and 1.7m using the GIS assisted positioning algorithms). Various indoor/outdoor experiments have been conducted using the RFID/MEMS INS/low-cost GPS integrated technique. It indicates that the techniques selected in this study, integrated with the low-cost GPS, can be used to provide continuous indoor/outdoor positions in approximately 4m accuracy with the iterated RSPKF. The results from the above experiments have demonstrated the improvements of integrating multiple sensors with RFID and utilizing the 3-D GIS data for personal positioning. The algorithms developed can be used in a portable RFID based multi-sensor positioning system to achieve metre-level accuracy in the indoor/outdoor environments. The proposed system has potential applications, such as tracking miners underground, monitoring athletes, locating first responders, guiding the disabled and providing other general location based services (LBS)

OPEN SOURCE WEB TOOL FOR TRACKING IN A LOWCOST MOBILE MAPPING SYSTEM

During the last decade several Mobile Mapping Systems (MMSs), i.e. systems able to acquire efficiently three dimensional data using moving sensors (Guarnieri et al., 2008, Schwarz and El-Sheimy, 2004), have been developed. Research and commercial products have been implemented on terrestrial, aerial and marine platforms, and even on human-carried equipment, e.g. backpack (Lo et al., 2015, Nex and Remondino, 2014, Ellum and El-Sheimy, 2002, Leica Pegasus backpack, 2016, Masiero et al., 2017, Fissore et al., 2018).<br><br> Such systems are composed of an integrated array of time-synchronised navigation sensors and imaging sensors mounted on a mobile platform (Puente et al., 2013, Tao and Li, 2007). Usually the MMS implies integration of different types of sensors, such as GNSS, IMU, video camera and/or laser scanners that allow accurate and quick mapping (Li, 1997, Petrie, 2010, Tao, 2000). The typical requirement of high-accuracy 3D georeferenced reconstruction often makes such systems quite expensive. Indeed, at time of writing most of the terrestrial MMSs on the market have a cost usually greater than 50000, which might be expensive for certain applications (Ellum and El-Sheimy, 2002, Piras et al., 2008). In order to allow best performance sensors have to be properly calibrated (Dong et al., 2007, Ellum and El-Sheimy, 2002).<br><br> Sensors in MMSs are usually integrated and managed through a dedicated software, which is developed ad hoc for the devices mounted on the mobile platform and hence tailored for the specific used sensors. Despite the fact that commercial solutions are complete, very specific and particularly related to the typology of survey, their price is a factor that restricts the number of users and the possible interested sectors.<br><br> This paper describes a (relatively low cost) terrestrial Mobile Mapping System developed at the University of Padua (TESAF, Department of Land Environment Agriculture and Forestry) by the research team in CIRGEO, in order to test an alternative solution to other more expensive MMSs. The first objective of this paper is to report on the development of a prototype of MMS for the collection of geospatial data based on the assembly of low cost sensors managed through a web interface developed using open source libraries. The main goal is to provide a system accessible by any type of user, and flexible to any type of upgrade or introduction of new models of sensors or versions thereof. After a presentation of the hardware components used in our system, a more detailed description of the software developed for the management of the MMS will be provided, which is the part of the innovation of the project. According to the worldwide request for having big data available through the web from everywhere in the world (Pirotti et al., 2011), the proposed solution allows to retrieve data from a web interface Figure 4. Actually, this is part of a project for the development of a new web infrastructure in the University of Padua (but it will be available for external users as well), in order to ease collaboration between researchers from different areas.<br><br> Finally, strengths, weaknesses and future developments of the low cost MMS are discussed

An Assessment on the Use of Stationary Vehicles as a Support to Cooperative Positioning

In this paper, we consider the use of stationary vehicles as tools to enhance the localisation capabilities of moving vehicles in a VANET. We examine the idea in terms of its potential benefits, technical requirements, algorithmic design and experimental evaluation. Simulation results are given to illustrate the efficacy of the technique.Comment: This version of the paper is an updated version of the initial submission, where some initial comments of reviewers have been taken into accoun

Jumps: Enhancing hop-count positioning in sensor networks using multiple coordinates

Positioning systems in self-organizing networks generally rely on measurements such as delay and received signal strength, which may be difficult to obtain and often require dedicated equipment. An alternative to such approaches is to use simple connectivity information, that is, the presence or absence of a link between any pair of nodes, and to extend it to hop-counts, in order to obtain an approximate coordinate system. Such an approximation is sufficient for a large number of applications, such as routing. In this paper, we propose Jumps, a positioning system for those self-organizing networks in which other types of (exact) positioning systems cannot be used or are deemed to be too costly. Jumps builds a multiple coordinate system based solely on nodes neighborhood knowledge. Jumps is interesting in the context of wireless sensor networks, as it neither requires additional embedded equipment nor relies on any nodes capabilities. While other approaches use only three hop-count measurements to infer the position of a node, Jumps uses an arbitrary number. We observe that an increase in the number of measurements leads to an improvement in the localization process, without requiring a high dense environment. We show through simulations that Jumps, when compared with existing approaches, reduces the number of nodes sharing the same coordinates, which paves the way for functions such as position-based routing