A key prerequisite for Precise Point Positioning (PPP) with Global Navigation Satellite Systems (GNSS) is the precise knowledge of satellite phase and code biases. This paper proposes a method, that is based on a very general measurement model with an individual phase and code bias for each receiver, satellite and frequency. We compute a recursive least-squares float solution with a Kalman filter, and a subsequent ambiguity fixed solution using Teunissen’s Best Integer Equivariant (BIE) estimator. The latter one minimizes the mean squared error (MSE) and, thus, outperforms the well-known Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method. Simulation results show the achievable performance of the BIE estimator in comparison to the LAMBDA method.     «

A key prerequisite for Precise Point Positioning (PPP) with Global Navigation Satellite Systems (GNSS) is the precise knowledge of satellite phase and code biases. This paper proposes a method, that is based on a very general measurement model with an individual phase and code bias for each receiver, satellite and frequency. We compute a recursive least-squares float solution with a Kalman filter, and a subsequent ambiguity fixed solution using Teunissen’s Best Integer Equivariant (BIE) estimat...     »