Integrity monitoring for kinematic precise point positioning in open-sky environments with improved computational performance

Positioning integrity monitoring (IM) is essential for liability- and safety-critical land applications such as road transport. IM methods such as solution separation apply multiple filters, which necessitates the use of computationally efficient algorithms in real-time applications. In this contribution, a new approach that significantly improves the computation time of the measurement update of the Kalman filter is presented, where only one matrix inversion is applied for all filters with measurement subsets. The fault detection and identification method and computation of the protection levels (PLs) are discussed. The computational improvement comes at the expense of a small increase in the PL. Test results for precise point positioning (PPP) with float ambiguities in an open-sky and suburban environment demonstrate the reduced computation time using the proposed approach compared to the traditional method, with 23%–42% improvement. The availability of IM for PPP, i.e. when the PL is less than a selected alert limit of 1.625 m, ranged between 92% and 99%, depending on the allowable integrity risk, tested at 10−5 and 10−6, and the observation environment.