A processing strategy for handling latency of PPP-RTK corrections

An attractive feature of PPP-RTK is the possibility of reducing the amount of data that needs to be transferred to users. By leveraging the state-space Representation (SSR) of the corrections, the correction provider (i.e., a GNSS network) can consider distinct transfer rates for each of the individual corrections according to their temporal characteristics. Reducing the transfer rates comes at the cost of delivering time-delayed corrections, urging the user to time predict the corrections to bridge the gap between the corrections' generation time and the positioning time. Consequently, the user Kalman filter needs to be equipped with a strategy to account for the errors caused by such predictions, minimizing the precision loss of the user parameter solutions. In this contribution, we apply a processing strategy for both the network and user filters to handle the latency of corrections. This enables the network to update corrections over longer time-intervals. To have the strategy applicable to regional networks, an ionosphere-weighted model is adopted for the corresponding observations, delivering minimum-variance spatially predicted ionospheric corrections to users. It is shown that certain components of the network filter's dynamic model are duplicated and should be excluded from processing. To illustrate the performance of the strategy at work, three globally distributed regional networks are employed, and maximum correction latencies to meet different positioning criteria are evaluated. In terms of both the positioning precision and time-to-first-fix (TTFF), the strategy is numerically shown to outperform the user processing case in which the uncertainty of corrections is discarded.