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In this paper, we present a novel PPP algorithm by applying the double difference for GNSS observables among multiple antennas (receivers), and apply improved VPPP algorithms. First of all, the GR models for double difference observables are shown which are similar to the GR models for the relative positioning algorithms, but both antennas' positions are unknown. Then we derive the Kalman filtering algorithms for recursive estimation of all antennas' positions and double difference integer ambiguity of all carrier-phases in GNSS observables. Then using the geometric constraints for all antennas' positions, we derive the algorithms of updating the estimated parameters including antennas' positions and integer ambiguities. Finally we show the experimental results of the proposed VPPP algorithm comparing with the previous VPPP algorithm.

In this paper, we study the VPPP (Very Precise Point Positions) algorithm based on GR (GNSS Regression) models by using multiple antennas for estimating baseline vectors. The multiple antennas are disposed with solid geometrical distances which provide the constraints of the estimated parameters, but both antennas' positions are unknown. By using the baseline-vector length constraints, we show the VPPP algorithms based on the double-difference (DD) observables by each pair of two antennas, and apply the algorithm for estimating baseline vectors and GNSS Gyro. Finally we show the experimental results of the estimation algorithms for the baseline vectors and Euler angles.

In this paper, we present a new relative positioning algorithm based on GNSS Regression measurement models (GR models) by using multiple antennas based on alternative use of the positions of reference receivers. We show the algorithms of estimating all unknown antenna positions by applying the double difference (DD). Then we derive updating algorithms by using true positions of the reference receivers. Finally, we carried out the experiments using real GNSS data and show the positioning results for the the proposed method as well as the conventional method.

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