In this work, a new GPS carrier phase-based\ud velocity and acceleration determination method is presented\ud that extends the effective range of previous techniques. The\ud method is named ‘EVA’, and may find applications in fields\ud such as airborne gravimetry when rough terrain orwater bodies\ud make difficult or impractical to set up nearby GPS reference\ud receivers. The EVA method is similar to methods such\ud as Kennedy (Precise acceleration determination from carrier\ud phase measurements. In: Proceedings of the 15th international\ud technical meeting of the satellite division of the Institute\ud of Navigation. ION GPS 2002, Portland pp 962–972,\ud 2002b) since it uses L1 carrier phase observables for velocity\ud and acceleration determination. However, it introduces\ud a wide network of stations and it is independent of precise\ud clock information because it estimates satellite clock\ud drifts and drift rates ‘on-the-fly’, requiring only orbit data\ud of sufficient quality. Moreover, with EVA the solution rate\ud is only limited by data rate, and not by the available precise\ud satellite clocks data rate. The results obtained are more\ud robust for long baselines than the results obtained with the\ud reference Kennedy method. An advantage of being independent\ud of precise clock information is that, beside IGS Final\ud products, also the Rapid, Ultra-Rapid (observed) and Ultra-\ud Rapid (predicted) products may be used. Moreover, the EVA\ud technique may also use the undifferenced ionosphere-free\ud carrier phase combination (LC), overcoming baseline limitations\ud in cases where ionosphere gradients may be an issue\ud and very low biases are required. During the development of\ud this work, some problems were found in the velocity estimation\ud process of the Kennedy method. The sources of the problems were identified, and an improved version of the\ud Kennedy method was used for this research work. An experiment\ud was performed using a light aircraft flying over the Pyrenees,\ud showing that both EVA and the improved Kennedy\ud methods are able to cope with the dynamics of mountainous\ud flight. A RTK-derived solution was also generated, and\ud when comparing the three methods to a known zero-velocity\ud reference the results yielded similar performance. The EVA\ud and the improved-Kennedy methods outperformed the RTK\ud solutions, and the EVA method provided the best results in\ud this experiment. Finally, both the improved version of the\ud Kennedy method and the EVA method were applied to a network\ud in equatorial South America with baselines of more\ud than 1,770 km, and during local noon. Under this tough scenario,\ud the EVAmethod showed a clear advantage for all components\ud of velocity and acceleration, yielding better and more\ud robust results.Peer ReviewedPostprint (published version
Precise corrections with a three-dimensional voxel model of the ionosphere based on global navigation satellite system (GNSS) data from a wide-area network of ground receivers can help resolve differential carrier-phase ambiguities over very long baselines of hundreds of kilometers in present two-frequency systems [global positioning system (GPS) and global orbiting navigation satellite system (GLONASS)] or in planned three-frequency systems (GALILEO, Modernized GPS). A study based on simulated three-frequency data from a modified GNSS signal generator indicates that all the phase ambiguities could be resolved successfully more than 90% of the time. This should be useful in surveying large areas with instruments that require very precise geolocation (e.g., radar or Lidar altimetry, interferometric synthetic aperture radar, interferometric sonar, etc.
The calibration errors on experimental Slant Total Electron Content (STEC) determined with Global Positioning System (GPS) observations is revisited. Instead of the analysis of the calibration errors on the carrier phase leveled to code ionospheric observable, we focus on the accuracy analysis of the undifferenced ambiguity-fixed carrier phase ionospheric observable determined from a global distribution of permanent receivers. The results achieved are: (1) By using data from an entire month within the last Solar Cycle maximum, the undifferenced ambiguity-fixed carrier phase ionospheric observable is found to be over one order of magnitude more accurate than the carrier phase leveled to code ionospheric observable and the raw code ionospheric observable. The observation error of the undifferenced ambiguity-fixed carrier phase ionospheric observable ranges from 0.05 to 0.11 TECU (Total Electron Content Unit) while that of the carrier phase leveled to code and the raw code ionospheric observable is from 0.65 to 1.65 TECU and 3.14 to 7.48 TECU, respectively. (2) The time-varying receiver DCB, which presents clear Day Boundary Discontinuity and intra-day variability pattern, contributes the most part of the observation error. This contribution is assessed by the short-term stability of the Between-Receiver DCB, which ranges from 0.06 to 0.17 TECU in a single day. (3) The remaining part of the observation errors presents a sidereal time cycle pattern, indicating the effects of the multipath. Further, the magnitude of the remaining part implies that the code multipath effects are much reduced. (4) The intra-day variation of the Between-Receiver DCB of the collocated stations suggests that estimating DCBs as a daily constant can have a mis-modeling error of at least several tenths of one TECU.
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.
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