We investigate the long-term performance and improvement of high-rate multiconstellation Global Navigation Satellite System (GNSS) or multi-GNSS attitude determination in a dynamical environment and evaluate how much each GNSS contributes to such an improvement with applications to land vehicle navigation. We build a multiantenna GNSS system with three nondedicated receivers and conduct two experiments with quad-constellations in GNSS-friendly and GNSS-challenged environments on January 19 and 23, 2018, respectively. A high-grade inertial measurement unit (IMU) provides a reference of comparison for dynamic attitude solutions. In the static case, the global positioning system (GPS), BeiDou navigation satellite system (BDS), and GLObal NAvigation Satellite System (GLONASS) single-constellation solutions have basically the same performance for both experiments. In the dynamic case, GPS achieves the best accuracy in the experiment on January 19, while on January 23, GPS and GLONASS achieve very similar accuracy and significantly outperform BDS in the pitch and roll components. The different dynamic performances of these single constellations are primarily attributed to their differences in observation noise level and satellite geometry strength. By combining GPS, BDS, GLONASS, and Galileo, the multi-GNSS solution performs significantly better than any single-constellation solution. The average improvements in the dynamic cases are 26.2%, 40.7%, and 36.0% on January 19 and 19.2%, 30.0%, and 36.2% on January 23 in the yaw, pitch, and roll components, respectively. The attitude solutions on January 19 are always better determined than on January 23 in the dynamic cases, which should clearly indicate the importance of combining multiple GNSS constellations and the benefit of more observed satellites and a stronger satellite geometry in GNSS-challenged environments. We also introduce the concept of “influence rate” and quantitatively analyze the contribution of each GNSS system to the combined attitude solution.

We present a new method of integrating Global Positioning System (GPS) and accelerometer data for high‐rate seismogeodesy. This method is based on the GPS variometric approach which can obtain seismic waves in real time using only the readily available broadcast products and a single receiver. Collocated 1‐Hz GPS and 200‐Hz accelerometer data from the 2016 M_W 7.8 Kaikōura earthquake were analyzed to verify the effectiveness of this method. Results reveal that this method can provide broadband and more accurate velocities and displacements qualified to detect P wave arrivals. Moreover, this method can effectively avoid the aliasing problem present in the 1‐Hz GPS waveforms. We therefore conclude that this new method can be a powerful tool to capture seismic waves in real time, which is crucial for the purpose of tsunami early warning and earthquake rapid response.

High-rate global navigation satellite system (GNSS) has emerged as an effective method to recover seismic waveforms without saturation and drifts, but it has the limitation of relatively lower sampling rate and higher noise level compared to seismic instruments. In this study, we present a new seismogeodetic method by integrating GNSS and accelerometer data to obtain optimal real-time seismic waveforms. Unlike traditional integration methods based on GNSS techniques of relative positioning or precise point positioning, the new method uses a GNSS time difference technique and inherits its unique advantage in real-time and high-accuracy velocity solutions. Furthermore, by incorporating the tightly coupled structure, it can overcome the cascading problem and provide more accurate and robust waveforms compared to its loosely coupled counterpart. The performance of this method is first compared with the traditional loosely coupled approach in challenging environments through a set of shake table experiments. With three GNSS satellites, this approach method can improve the accuracy of velocities and displacements by 42 and 87 per cent, respectively. With four or more GNSS satellites, the average improvements of the method reach 25 and 41 per cent for the velocities and displacements, respectively. We then validate the full performances of the method through simulated shake table experiments and collocated GNSS and accelerometer data during the 2016 M_w 6.6 central Italy earthquake. The simulated and real-event analyses demonstrate that the new integration method can take full advantage of the complementary characteristics of GNSS and accelerometer sensors. By providing more accurate and broad-band velocity and displacement waveforms in a real-time or near-real-time manner, this method is quite promising in earthquake early warning and rapid source inversion.