Self-calibration and Collaborative Localization for UWB Positioning Systems

Abstract

Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.

Indoor Positioning Systems: A Blessing for Seamless Object Identification, Monitoring, and Tracking

“…Contemporary research proves that UWB technology is largely used for industries and manufacturing sites where it could track and trace both static and dynamic objects at ease. Though its utility is large, the signals of UWB are easily hindered by the indoor obstacles, thereby making error mitigation a necessity ( 14 ). Conventional methods such as WiFi and Bluetooth are often considered less often as it offers low range.…”

An Experimental Study of Two-way Ranging Optimization in UWB-based Simultaneous Localization and Wall-Mapping Systems

“…, where m = 1, 2, or 3. Furthermore, the difference of the LoS and the reflected paths is dT m = q (x t x r ) 2 +(y t y r ) 2 q (x vm x r ) 2 +(y vm y r ) 2 , where m =1 , 2, or 3.…”

“…In recent years, precise indoor localization has been widely studied, which provides real-time tracking services in emergent scenarios, including military security, home surveillance, and medical supervision [1]. Thanks to small size, low cost, and high signal penetration, ultra-wideband (UWB) radio transceivers are used in wireless localization systems to track people or animal movements [2]. In the UWB-based localization system, a UWB tag is attached to the tracking target, as shown in Fig.…”

Posture algorithm for mobile robot based on ultrawide band

“…The TOA positioning algorithm is very simple, and its hardware requirements are relatively low. 5,6 The TDOA positioning algorithm obtains the positioning information by recording the TDOA between the tag and different base stations. Although the positioning accuracy of TDOA is better than that of TOA, the hardware requirements of the former are much higher than that of the latter, and the clock synchronization between the tag and the base station is needed.…”

“…The TOA positioning algorithm is very simple, and its hardware requirements are relatively low. 5,6 The TDOA positioning algorithm obtains the positioning information by recording the TDOA between the tag and different base stations.…”