Precise Indoor Positioning and Attitude Determination using Terrestrial Ranging Signals

Self Cite

Global Navigation Satellite System (GNSS) is the most widely used Positioning, Navigation, and Timing (PNT) technology in the world today, but it suffers some major constraints. Locata is a terrestrial PNT technology that can be considered as a type of localised “constellation”, which is able to provide high-accuracy PNT coverage where GNSS cannot be used. This paper presents a comprehensive literature review of the Locata technology and its applications. It seeks to answer questions, such as: (1) What is Locata and how does it work? (2) What makes Locata unique compared with other terrestrial positioning systems? (3) How has Locata been used in different applications for accurate PNT? (4) What are the current challenging issues that may restrict its further adoption for custom-grade navigation in urban environments?

Section: Locata Core Technologiesmentioning

confidence: 99%

“…More recently, fault-tolerant decentralised GNSS/Locata/INS integration algorithms were investigated for land vehicle navigation [46]. To mitigate the multipath effects, VRay antenna was designed and applied for vehicle tracking, and investigations regarding the positioning algorithms were detailed in [31,32].…”

Section: Locata Applicationsmentioning

confidence: 99%

Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology

Rizos

Yang

2019

Self Cite

“…Although widely used global navigation satellite systems (GNSS) could provide a relatively high positioning accuracy outdoors, it does not perform well indoors due to the coverage limitation of satellite signals. Therefore, various indoor positioning technologies have been proposed in order to enable high-accuracy localization in indoors over the past two decades, including infrared [4], ultrasonic [5], ultra-wideband (UWB) [6], pseudolite [7], acoustic signals [8], Wi-Fi [9,10], Bluetooth [11], etc. However, indoor high-accuracy positioning is still an open but challenging issue, which has been proved by the conclusion “the indoor location problem is not solved” from the recent indoor localization competitions hosted by Microsoft [12].…”

Section: Introductionmentioning

confidence: 99%

CC-DTW: An Accurate Indoor Fingerprinting Localization Using Calibrated Channel State Information and Modified Dynamic Time Warping

Deng

Cheng

et al. 2019

Indoor wireless local area network (WLAN) based positioning technologies have boomed recently because of the huge demands of indoor location-based services (ILBS) and the wide deployment of commercial Wi-Fi devices. Channel state information (CSI) extracted from Wi-Fi signals could be calibrated and utilized as a fine-grained positioning feature for indoor fingerprinting localization. One of the main factors that would restrict the positioning accuracy of fingerprinting systems is the spatial resolution of fingerprints (SRF). This paper mainly focuses on the improvement of SRF for indoor CSI-based positioning and a calibrated CSI feature (CCF) with high SRF is established based on the preprocess of both measured amplitude and phase. In addition, a similarity calculation metric for the proposed CCF is designed based on modified dynamic time warping (MDTW). An indoor fingerprinting method based on CCF and MDTW, named CC-DTW, is then proposed to improve the positioning accuracy in indoors. Experiments are conducted in two indoor office testbeds, and the performances of the proposed CC-DTW, one time-reversal (TR) based approach and one Euclidean distance (ED) based approach are evaluated and discussed. The results show that the SRF of CC-DTW outperforms the TR-based one and the ED-based one in both two testbeds in terms of the receiver operating characteristic (ROC) curve metric, and the area under curve (AUC) metric.

“…Meanwhile, the demands for accurate localization in indoor environments have increased dramatically [ 2 , 3 ]. Various positioning technologies, including Infrared [ 4 ], Ultrasonic [ 5 ], Ultra-Wideband (UWB) [ 6 ], Pseudolite [ 7 ], Wireless Local Area Network (WLAN) [ 8 ], and Bluetooth Low Energy (BLE) [ 9 ], have been proposed, aiming to improve the localization performance indoors. BLE positioning has aroused great research attention recently because of its stable signal with low fluctuation, easy implementation with low energy cost, low computational complexity, and high mobility [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

An IMU-Aided Body-Shadowing Error Compensation Method for Indoor Bluetooth Positioning

Deng

Wang

2018

Research on indoor positioning technologies has recently become a hotspot because of the huge social and economic potential of indoor location-based services (ILBS). Wireless positioning signals have a considerable attenuation in received signal strength (RSS) when transmitting through human bodies, which would cause significant ranging and positioning errors in RSS-based systems. This paper mainly focuses on the body-shadowing impairment of RSS-based ranging and positioning, and derives a mathematical expression of the relation between the body-shadowing effect and the positioning error. In addition, an inertial measurement unit-aided (IMU-aided) body-shadowing detection strategy is designed, and an error compensation model is established to mitigate the effect of body-shadowing. A Bluetooth positioning algorithm with body-shadowing error compensation (BP-BEC) is then proposed to improve both the positioning accuracy and the robustness in indoor body-shadowing environments. Experiments are conducted in two indoor test beds, and the performance of both the BP-BEC algorithm and the algorithms without body-shadowing error compensation (named no-BEC) is evaluated. The results show that the BP-BEC outperforms the no-BEC by about 60.1% and 73.6% in terms of positioning accuracy and robustness, respectively. Moreover, the execution time of the BP-BEC algorithm is also evaluated, and results show that the convergence speed of the proposed algorithm has an insignificant effect on real-time localization.