Peirong Fan

This paper presents a two-step stochastic hybrid estimation (TS-SHE) algorithm for robust and accurate carrier phase tracking of Global Navigation Satellite System (GNSS) signals in urban environments with significant power degradation and fluctuation. The proposed algorithm adaptively combines a bank of parallel Kalman filters with different dynamic state models to cope with the non-stationarity of GNSS signals. Different from conventional approaches including the phase lock loop (PLL), the Kalman filter (KF) and the interacting multiple model (IMM) method, which strongly rely on the a priori fixed signal model to ensure good tracking performance, we develop a novel stochastic filter transition strategy utilizing the information from reliable signal condition evaluation to overcome the performance degradation caused by the model mismatch in urban environments. Therein, for the first time we use the prior information of signal fading conditions for more accurate combined weighting of all component filters. Specifically, to obtain the prior information, GNSS data are sequentially buffered and analyzed in the first step. Then in the second step, optimized filter weights are generated and the overall combined carrier phase is estimated. We analyze the theoretical performance of the new algorithm and show the effects of different design parameters on the performance. The primary advantages of the proposed algorithm include: 1) the ability to rapidly recover carrier phase observation when blocked signals are re-acquired, and 2) high accuracy in carrier phase estimation of GNSS signals corrupted by strong multipath fading. Simulation and real data experiment results show the enhanced robustness and improved accuracy of the proposed TS-SHE algorithm, compared with conventional carrier phase tracking methods.

Robust and centimeter-level Real-time Kinematic (RTK)-based Global Navigation Satellite System (GNSS) positioning is of paramount importance for emerging GNSS applications, such as drones and automobile systems. However, the performance of conventional single-rover RTK degrades greatly in urban environments due to signal blockage and strong multipath. The increasing use of multiple-antenna/rover configurations for attitude determination in the above precise positioning applications, just as well, allows more information involved to improve RTK positioning performance in urban areas. This paper proposes a dual-antenna constraint RTK algorithm, which combines GNSS measurements of both antennas by making use of the geometric constraint between them. By doing this, the reception diversity between two antennas can be taken advantage of to improve the availability and geometric distribution of GNSS satellites, and what is more, the redundant measurements from a second antenna help to weaken the multipath effect on the first antenna. Particularly, an Ambiguity Dilution of Precision (ADOP)-based analysis is carried out to explore the intrinsic model strength for ambiguity resolution (AR) with different kinds of constraints. Based on the results, a Dual-Antenna with baseline VEctor Constraint algorithm (RTK) is developed. The primary advantages of the reported method include: (1) Improved availability and success rate of RTK, even if neither of the two single-antenna receivers can successfully solve the AR problem; and (2) reduced computational burden by adopting the concept of measurement projection. Simulated and real data experiments are performed to demonstrate robustness and precision of the algorithm in GNSS-challenged environments.

Traditional attitude determination using global navigation satellite system (GNSS) carrier phases is mostly applied on geodetic-grade receivers with sufficient baseline length. However, for some special applications such as mobile communication base station smart antenna attitude determination, only low-cost receivers with ultra-short baselines can be employed, and the environments are more challenging. When solving the ambiguity resolution (AR) problem with low-cost receivers, it is hard for the traditional methods in ambiguity domain to estimate float ambiguities accurately due to the large code pseudorange noises; thus, such systems fail to determine the correct ambiguities. Aiming at improving the AR success rate for ultra-short baselines attitude determination with low-cost receivers, we provide an objective function named Mean Square Residual (MSR) based on the geometrical relationship among the position spherical search space, the fractional carrier phases, and the possible ambiguities. The method can be calculated without code pseudoranges, and thus, can provide a higher AR success rate when using low-cost receivers. The corresponding analysis and acceptance test method are discussed in this contribution, and further, as an extension for more complicated urban dynamic applications, a GNSS/Inertial Navigation System (INS) integrated system is introduced. Several experiments have been conducted to verify performance.