Jeonghyeon Yun, Cheolsoon Lim, Byungwoon Park, Sejong University, South Korea; Jade Morton, University of Colorado, Boulder

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In the ionosphere, there are dense free electrons because the atmosphere is very thin. Free electrons are very high energy and, unlike stable neutral atoms, are not in plasma state, but consist of partially negatively charged electrons and positively charged cations. Therefore, when a radio wave such as a GNSS (Global Navigation Satellite System) signal passes, refraction and diffraction may occur, which can seriously affect the amplitude and phase of the radio waves, which is called ionospheric irregularity. This greatly affects the quality and accuracy of the GNSS signals. There are several methods to detect ionospheric irregularity using signal degradation due to the ionospheric effect, such as the use of amplitude variation detection and the use of carrier measurements. In general, the above indicators have a problem of using an expensive receiver that outputs data at a high speed of 50 Hz or more, or a dedicated ionospheric monitoring receiver for measuring signal strength. A relatively practical and easy methodology has been suggested to detect irregularities and to estimate their horizontal velocities using Rate of Total Electron Content (ROT) signature and their correlation of receiver network in 2019 (Park, 2019), This paper introduces that this ROT-based technique can detect ionospheric irregularity using low-cost android smartphones without using ionospheric monitoring dedicated receivers. ROT can be estimated by time difference of dual frequency carrier phase based on that ionospheric errors included in GNSS carrier-phase measurements are reflected differently according to frequency. Geometry-free combination of dual frequency carrier phase can extract precise ionospheric error from other geometry terms such as distance between a satellite and a receiver, satellite and receiver clock error, satellite orbit error and tropospheric error, even though it includes unknown bias. As long as any cycle slip does not occur the unknown bias is kept constant, and accurate ROT can be calculated by time differentiating the biased precise ionospheric error estimation. The ROT-based ionospheric irregularity detection technique can be used without the inherent knowledge of GNSS or sophisticated bias estimation using a relatively simple formula and is suitable for detecting small fast-moving irregularities due to its high resolution and high rate characteristics. In normal condition at mid-latitude region, ROT variation is very similar to white noise with an average close to zero. When a GNSS signal passes through the ionospheric irregularity, however, the ROT value increases and then decreases shortly because the signal goes from low density to high density and then low density again. Accordingly, the signal that passed through the ionospheric irregularity remain a unique pattern, and the horizontal velocity of the irregularity can be estimated by correlating the patterns obtained from receiver network. One thing we should consider when we use this method is that the irregularity detection depends on the relative magnitude of ionospheric irregularity gradient and the receiver noise level. Android devices could be one option to detect the ionospheric irregularity and recently released flagship smartphones (Galaxy S10 & Note10, Pixel 4, Xiaomi Mi8 & mi9, Huawei P30 Pro etc.) of leading brands such as Samsung, Google, Xiaomi, and Huawei are providing dual-frequency (L1/E1 and L5/E5) measurements. As the needs for high accurate position information in smart devices increases, more future smartphones are likely to support dual-frequency. To check the feasibility of the Android device detecting the irregularity, we conducted an experiment at the Alaska Fairbanks Poker Flat Research Range where geomagnetic storms occur often. We installed three Xiaomi phones close to a ground reference station at the Poker Flat and collected Android GNSS data on August 7 to 9, 2019. Noise level of ROT estimates of android smartphones is about 1.3 cm/s(1?) and the ROT values of the three locations rose up to 20 cm/s then then fall to -15 cm/s at 2019/08/09 08:29:00 UTC, which was confirmed as a proper detection after comparing with the result of a near Septentrio PolaRX receiver. Therefore, we can conclude that the ionospheric irregularity can be detected even with android smartphones, when its ROT is larger than 4 cm/s (3?). To avoid false detections due to instability of GNSS measurements and inherent frequent cycleslips of android smartphones, it is necessary to compare the ROTs of a lot of smartphones. Two extra smartphones installed near were very effective to find out the real irregularity from many false alarms. Based on this experimental results and analysis, we suggest a data clouding server that can collect GNSS data from multiple smartphones in different regions of the world and detect ionospheric irregularity by applying ROT-based technique to the collected data. To avoid personal privacy problem, we collect Android L1/L5 carrier phase measurement and NMEA position information without any personal information. The gathered data is sent to the module that calculate ROT for each device, and then determine if an irregularity has occurred at current time after comparing the ROT values estimated by near devices. We expect the suggested Android GNSS data clouding server to be a dense network enough to catch a small-scaled local ionospheric irregularity. [1] Byungwoon Park, Cheolsoon Lim, Sejong University, South Korea; Jun Wang, National Oceanic and Atmospheric Administration; Y. Jade Morton, University of Colorado Boulder, A New Method to Estimate Ionospheric Irregularity Drift Velocity Using ROT Variation and Spaced GNSS Reference Stations:, ION GNSS+ 2019