A Kalman Filter-Based Short Baseline RTK Algorithm for Single-Frequency Combination of GPS and BDS

Abstract

The emerging Global Navigation Satellite Systems (GNSS) including the BeiDou Navigation Satellite System (BDS) offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK) algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.

Multiple Faulty GNSS Measurement Exclusion Based on Consistency Check in Urban Canyons

“…The error of each satellite is assumed to be independent of each other. With the weighted least squares (WLS) estimation, the receiver states can be estimated using (4).…”

“…Global positioning system (GPS) sensor provides absolute localization service in all outdoor areas in all weather conditions [3]. In open-sky areas, GPS sensor can achieve up to sub-meter positioning accuracy if differential correction is available [4,5]. However, particularly in Asian highly-urbanized city, dense skyscrapers and narrow streets strongly challenge the GPS localization performance in two aspects, blockage and reflection [6].…”

Investigating multi-GNSS performance in the UK and China based on a zero-baseline measurement approach

“…For instance, the introduction or exclusion of an ascending or descending satellite, of low elevation angle and/or low Signal-to-Noise ratio (SNR) may lead to a jump in the position time series. Likewise jumps are present when moving from fixed to float solution of the ambiguity resolution in sequent durations, as the new measurements introduce an inconsistency into the Kalman filter solution [15]. These jumps, which affect more the vertical than the horizontal components, can be removed by excluding certain satellites of lower SNR during the periods when jumps are present; This lead to improve the STD of Up component to be 2.5mm.…”

“…The combination of two or more satellite systems offers more visible satellites to users, and that will enhance the satellite geometry with the expectation of improving the overall positioning solution [12,13]. A number of studies have generally shown that the contribution of combined GNSS systems improves the positional accuracy and increases the rate of fixing ambiguity resolution [14,15]. For instance, Cai and Gao [16] have indicated an improvement in the positional accuracy when combining simulated GPS and GLONASS observations as compared to GPS-only solution.…”

A Dual Frequency Carrier Phase Error Difference Checking Algorithm for the GNSS Compass

“…The carrier phase measurement observed by the two receivers at a certain epoch can be written as [35]: ${\varphi }_{b,k}^i={\lambda }_k^{-1}\left(r_{b,k}^i-I_{b,k}^i+T_{b,k}^i\right)+f_k\left(\delta t_{b,k}-d{t}_k^i\right)+N_{b,k}^i+{\epsilon }_{b,k}^i$ ${\varphi }_{r,k}^i={\lambda }_k^{-1}\left(r_{r,k}^i-I_{r,k}^i+T_{r,k}^i\right)+f_k\left(\delta t_{r,k}-d{t}_k^i\right)+N_{r,k}^i+{\epsilon }_{r,k}^i$ where $\varphi$ is the carrier phase measurement (unit: cycle), $\lambda$ is the carrier wavelength (unit: m), $r$ represents the true geographical distance between the satellite and the receiver (unit: m), $I$ is the ionospheric delay (unit: m), $T$ is the tropospheric delay (unit: m), $f$ is the carrier frequency (unit: Hz), $\delta t$ is the receiver clock error (unit: s), $dt$ is the satellite clock error (unit: s), $N$ is the integer ambiguity (unit: cycle) and $\epsilon$ is the residual errors mainly including carrier phase noise and multipath (unit: cycle). Subscripts $b$ and $r$ respectively represent the base and the rover receiver.…”