Recently, research and business communities show a great interest in the Local Positioning System (LPS) technology. Unlike the Global Positioning System (GPS, Galileo, GLONASS, QZSS), LPS systems allow indoors localization. In order to locate an object within a certain area, a wireless infrastructure needs to be installed. As a rule, indoors, the localization accuracy depends on the spatial density of anchor nodes. Those anchors are used to measure distances to mobile nodes.
Real-time positioning systems are based on wireless networks which can utilize different methods of distance of measurement: Time-of-Flight (ToF), Angle-of-Arrival (AoA) and Received Singal Strength (RSS). Methods based on Time-of-Arrival (ToA), Time-Difference-of-Arrival (DoA), Round-Trip-Time (RTT) are referred to the ToF methods group. First two methods (ToA and TDoA) do require sy;stem time synchronization between all nodes in the system, whereas the RTT method does not. Obviously, distance measurements based on the RSS are relatively inaccurate, especially in a case of substantial distance between nodes. However, knowing of the RSS value is very important for applications with room-level accuracy indoors: room walls create a drop in a signal strength, which is used to reliably determine the room where the mobile object in is. This technique is widely used in RSS patterns methods [1,2].
Along with the localization capability, wireless networks provide data communications channels between nodes. Commercial LPSs use various radio technologies: Wi-Fi, ZigBee, UWB, nanoLOC, NFC RFID, etc. This paper presents an overview of the RealTracTM technology developed by the RL-Service ltd. It is based on the nanoLOC (IEEE 802.15.4a) radio standard. The RealTracTM technology combines good data transfer rate with low power consumption of radio devices, location estimation and voice communication feature at the same time.
The rest of this paper is organized as follow. Section 2 describes architecture of the RealTracTM technology; technical characteristics of devices used; data transfer protocols for communication between radio modules in the system and between a client and a server. Section 3 is devoted to the applied location estimation algorithms based primarily on the particle filter. ToF and RSS values, building structure, constraints on object velocity and data acquired from the embedded inertial measurement unit (IMU) are taken into a consideration. The opportunity of using the precise air pressure sensor is utilized for the floor identification and for the estimation of the relative height. Those features are based on the atmospheric pressure data of all devices in the system. Section 4 concludes the development work and briefly describes possible applications of the described technology and defines future development directions.
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