Characterization of Real Multi-Correlator Outputs for SQM Performance Evaluation

Signal Quality Monitoring is a process put in place in augmentation systems such as SBAS or GBAS to monitor potential signal distortions with high integrity that may be created by a satellite failure. It generally consists in the combination of several correlator outputs in so-called metrics, such as the single ratio metrics, the symmetric ratio metric or the double different metrics. To validate the compliance of a particular combination of metrics, it is necessary to validate the detection performance of an SQM process against every possible distortions of a Threat Space, in presence of typical errors affecting the metrics. Usually, theoretical models are used in order to simulate the error affecting the correlator outputs and the metrics. However, those models cannot fully capture the diversity of the errors, such as the temporal correlation of multipath, or its effects on close correlator outputs. It is therefore of high interest to use real data collect in order to derive the models of the correlator output models, to validate the compliance of an SQM in operational conditions. ENAC has put in place an automated data collect in order to observe the distribution of correlator output errors over a long period. Due to the large variation of the number of low-elevation satellites in a day, this scheduling task requires a specific process to collect as many observations as possible from low-elevation satellites in a limited period of time. An optimization algorithm, adapted from the simulated annealing process, allows to find an optimal scheduling, taking into account the constraint of the long post-processing task of the collected digitized samples by a software receiver. By accumulating a large set of correlator outputs from low-elevation satellites, an accurate distribution of the covariance matrix of the correlator outputs is obtained, capturing all the effects occurring in the real world and in a real receiver. Applying this distribution in the SQM compliance test can help to have a more realistic performance. The comparison of an SQM performance between theoretical and observation-based models shows some major differences.