Title: VPPP Algorithms with Multiple Antennas and Highly Accurate Attitude Estimation by the Ambiguity Resolution Methods
Author(s): G. Okuda, A. Mouri, H. Hasegawa, Y. Arakawa, Y. Kubo, S. Sugimoto
Published in: Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
Portland, Oregon
Pages: 2244 - 2262
Cite this article: Okuda, G., Mouri, A., Hasegawa, H., Arakawa, Y., Kubo, Y., Sugimoto, S., "VPPP Algorithms with Multiple Antennas and Highly Accurate Attitude Estimation by the Ambiguity Resolution Methods," Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2244-2262.
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Abstract: In consequently to the previous investigations, we derive the algorithms for Very Precise Point Positioning (VPPP) using multiple GNSS antennas by applying the double difference for GNSS observables. Simultaneously, in this paper, we present new algorithms of estimating baseline vectors between each two antennas by applying the several ambiguity resolution methods such that we can derive the highly accurate attitude estimation algorithms. We have been developing VPPP algorithms using multiple antennas based on GNSS Regression measurement models (abbreviated as GR models). In this paper, first, GR models for the double difference for GNSS observables by multiple antennas are shown which are similar to the GR models for the relative positioning algorithms, but all antennas’ positions are unknown, with solid geometrical distances of antennas. Then we derive the Kalman filtering algorithms for recursive estimation of the all antennas’ positions. Then using the geometric constraints for all antennas’ positions and based on analyses of conditional probability, we show the algorithms of updating the estimated parameters including antennas’ positions and double difference integer ambiguities by applying the Kalman filter based on the ambiguity resolution methods. After estimating the highly accurate baseline vectors, we derive the attitude estimation algorithms. Finally, we show the experimental results of applying the present VPPP algorithms with geometric constrains (4 antennas are deployed at climaxes of a square with 80cm sides) for real measurement data from low-cost L1 receivers, only using the broadcast navigation data, without using any correction information (any external transmitted information). Presently, less than 50cm RMS positioning errors are achieved for ten seconds (10 epochs) GNSS observables and 0.2 degree errors of Euler’ s angles for the attitude estimation.