Inside GNSS Media & Research

NOV-DEC 2017

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58 Inside GNSS N O V E M B E R / D E C E M B E R 2 0 1 7 can be classified into two main groups: GNSS+INS and GNSS+SoO. Currently, hybrid localization with GNSS and SoO, such as Long Term Evolution (LTE), 5G, UWB, and WLAN, is a hot topic in the localization field. In the GNSS+INS integration, the short-term stable INS data complements long-term stable GNSS data. ese sys- tems can provide more accurate and precise location information than a single system, also yielding information during a possible outage of one system. In GNSS+INS hybrid localization, the inertial information provided by the Micro-Electro-Mechanica l Systems (MEMS) provides location relative to a previous location at high rate. Other devices such as cameras, radar, barom- eters, and many more deliver absolute position information which can also be synchronized with the GNSS signal. Due to this hybridization, the accuracy and ubiquity of the location service is boost- ed in indoor and urban environments (due to the non-GNSS technologies) while still maintaining the outdoor sce- narios (thanks to GNSS technologies). However, INS systems usually require an initial calibration, and they accumulate position error with time. For GNSS+INS integration, three principle approaches exist: i) loose cou- pling (combines a GNSS derived posi- tion with INS data), ii) tight coupling (integrates GNSS pseudoranges and INS data), and iii) deep coupling (involves INS data in the GNSS signal tracking). Regarding the location privacy of an end user, pure GNSS+INS systems are commendable because only the user equipment aggregates and processes data of both subsystems; no third party is involved. Similar integration concepts can also be found for the integration of GNSS with terrestrial communication systems. Hybridization of position level is always possible. Many examples for a tighter integration exist as well. For example, GNSS+LTE combines GNSS technolo- gies and cellular-specific technologies, including Observed Time Difference of A rriva l (OTDoA), Upli n k-Ti me Difference of Arrival (U-TDoA), and Enhanced-Cell ID (E-CID) to provide a more robust and ubiquitous localiza- tion service. Secondly, GNSS+UWB mixes GNSS positioning technologies and UWB technologies, which usually perform Time of Arrival (TOA) tech- niques. irdly, GNSS+WLAN merges GNSS and WLAN technologies, which oen employ Received Signal Strength (RSS)-based techniques, to enhance the performance of the localization service. A sub-group of GNSS+SoO is a het- erogeneous IoT system, where GNSS sensors and terrestrial IoT sensors are combined. GNSS technologies suited for IoT receivers are hindered by the requirements of low computationa l power and low power consumption, which might clash with the compu- tational requirements of GNSS signal processing, thus resulting in a faulty localization service in severe working conditions. In this sense, vendors are developing ultra-low power GNSS mod- ules aimed for IoT mass-market devices, with the objective of providing high accuracy with low-powered sensors. Many studies have been carried out evaluating these hybrid systems, in par- ticular in autonomous vehicle applica- tions (see J. A. Peral-Rosado et alia in Additional Resources), where security and privacy are mandatory to avoid life-or-death scenarios occasioned by attackers. As hybrid systems use GNSS and non-GNSS technologies, they also suffer from the same security and priva- cy threats as pure GNSS and pure non- GNSS technologies, and thus the same solutions may be applied (see Location Privacy Challenges and Solutions, Part 1 published in Inside GNSS September/ October 2017 as well as later sections of this article). However, as the num- ber of systems in use increases, so does the probability of suffering an attack. In addition, the software required to carry out the hybrid positioning tech- niques must offer security and privacy, provided by the usual security soware. If not, this soware becomes a security breach in the hybrid system that can be exploited by attackers. The threats to location privacy in GNSS+SoO are more obscure and possi- bly more abundant compared to the case of GNSS+INS because more parties and communication between those parties are involved. e parties involved in a hybrid GNSS positioning system are: the GNSS space segment, the user's device and the network segment, including the Location Service Provider (LSP), the anonymizer, and the Location Based Service Provider (LBSP), as illustrated in Figure 1. In practice, some of the units shown in Figure 1 can be merged or absent. e main GNSS data regard- ing the user localization comes from the satellites. Non-GNSS data for localiza- tion comes from the LSP. Nonetheless, WORKING PAPERS FIGURE 1 The main entities involved in a typical hybrid GNSS localization system Service provision Service request Data for user centric- positioning and user-centric loose/tight fusion Data for network-centric position and network-centric loose/tight fusion Navigation messages User device with hybrid GNSS location engine Location-Based Service Provider (LBSP) Anonymizer Location Service Provider (LSP) Terrestrial positioning system Any of these can be collocated; equally, they can be three independent entities Multi-GNSS constellation (Galileo, Compass/Beidou, GPS, Glonass) e.g., company providing the software (and possibly the hardware) for indoor localization

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