GNSS-A observation model with attached oceanic slowness gradient

The Global Navigation Satellite System-Acoustic (GNSS-A) geodetic observation model needs to consider the spatiotemporal variation of sound velocity to achieve centimeter-level precision positioning. Most existing research has focused on refining GNSS-A observation models by examining the four-dimensional sound velocity field in the ocean. In fact, it is more directly effective to discuss the construction of high-precision positioning models from the perspective of the four-dimensional slowness field in the ocean. This paper investigates the impact of oceanic slowness variation on seafloor geodetic positioning. Assuming a single-layer slowness field (SSF), a GNSS-A observation model with an attached slowness gradient is proposed. This model has significant advantages in simplifying the observation model formula and improving the efficiency of model solving. The proposed model and algorithm are validated through simulated GNSS-A data and offshore experimental datasets. The results show that the positioning accuracy in deep sea using the observation model with an attached slowness gradient can reach centimeter-level, comparable to the traditional sound velocity error compensation model. It has the same effectiveness as traditional sound velocity parameter estimation but with higher computational efficiency.