A Run-Time Method Based on Observable Data for the Quality Assessment of GNSS Positioning Solutions

Several location-aware applications rely on the position estimated by means of Global Navigation Satellite Systems (GNSS), which are known to estimate an accurate position in an open environment. However, the quality of the estimated position is degraded in harsh environments in terms of accuracy and reliability. Liability-critical services, such as location-based charging, transportation, and road tolling, are threatened by the use of an unreliable position of the user, and the level of trust in the estimated position has to be considered to avoid a failure of the full service chain. Such an issue is faced by means of integrity monitoring methods in the field of GNSS. However, when dealing with harsh environments, integrity monitoring techniques designed for aeronautical applications would lead to very conservative results and to the rejection of all the positions obtained. Such conservative approach is based on the theoretical error models for the estimation of the pseudorange standard deviation in open sky. The purpose of this work is to propose an innovative method for estimating the pseudorange standard deviation extrapolating it from measurements of observable data, to assess the confidence level in the obtained positions in relation to the real environment surrounding the user. While measuring the pseudorange standard deviation taking into account environment conditions and receiver accuracy, the user is able to achieve better estimation of the user equivalent range error (UERE). Estimating the UERE from raw pseudorange measurements with the proposed run-time method and its subsequent use in the computation of protection levels using the receiver autonomous integrity monitoring (RAIM) algorithm shows significant improvement in navigation system availability by deriving tight protection levels.