A Method to determine secondary codes and carrier phases of short snapshot signals

Recently, the Snapshot Real-Time Kinematic (SRTK) technique was demonstrated, which aims at achieving high accuracy navigation solutions with a very short signal collection. The main challenge in implementing SRTK is the generation of valid carrier-phase measurements, which relies on a data bit ambiguity (DBA) resolution process. For pilot signals, this step is equivalent to the correct selection of secondary code indexes (SCIs) from the ambiguous sets obtained from a multi-hypotheses (MH) acquisition process. Currently, SCI ambiguities are solved independently for each satellite. However, this method is ineffective when the snapshot signal is relatively short. In order to tackle this problem, this article proposes a new method that makes use of assistance data and processes information from all satellites to jointly solve the DBA issue. This new method is shown to be more effective in determining the correct SCI and enabling valid snapshot carrier-phase measurements, largely expanding the scope of high-accuracy snapshot positioning.This research was supported by the Albora Technologies and Universitat Politècnica de Catalunya with industrial PhD grant number DI 082 from the Generalitat de Catalunya and the project RTI2018-094295-B-I00 funded by the MCIN/AEI 10.13039/501100011033 which is co-funded by the FEDER programme. P.C. has been partially supported by the NSF under Awards CNS-1815349 and ECCS-1845833.Peer ReviewedPostprint (published version

Cloud-based single-frequency Snapshot RTK positioning

With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km.This research was funded by Albora Technologies and Universitat Politècnica de Catalunya with industrial PhD grant number DI 082 from the Generalitat de Catalunya; This research was partially funded by the Spanish Ministry of Science and Innovation project RTI2018-094295-B-I00. P.C. has been partially supported by the NSF under Awards CNS-1815349 and ECCS-1845833Peer ReviewedPostprint (published version

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot