Adaptive Smoothing with Ambiguity Fixation for GNSS Post-Processing


We propose a complete post-processing solution for GNSS (global navigation satellite system) positioning leveraging linear-regression Kalman filtering methods, employed in a Rauch- Tung-Striebel (RTS) smoothing context, and adapting the model parameters in an expectation maximization (EM) framework. In particular, we (i) discuss the effects of using different moment approximations in the smoother; (ii) demonstrate that it is advantageous to fixate the integer ambiguities on the smoothing posterior; and (iii) show that the proposed method is viable for a wide range of GNSS measurement models, including single difference, double difference, and ionosphere-free combinations of the multi-band GNSS observations.


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