Metrics for Evaluating GNSS Real-Time High(er)-Rate Performance

Date: 4/25/2019

Time: 06:00 PM

Room: Grand Ballroom

The Network of the Americas (NOTA) continuously operating Global Navigation Satellite System (GNSS) network is a federated network of the NSF funded EarthScope Plate Boundary Observatory, COCONet, and NSF/UNAM TLALOCNET. When built, these foundational networks recorded 15-30s data in 24-hour files for the primary purpose of a daily point position. Data quality was primarily evaluated in the pre-processed data using metrics developed in the 1990’s based on 15-30s interval data.

Advances in GNSS technology as well as data processing now provide position solutions with cm-level precision at high-rate (>=1sps) and low latency (<1s). NOTA currently has ~850 GNSS stations streaming 1sps to the UNAVCO operations center in Boulder, CO. Applications of the streamed 1sps data include large magnitude earthquake and tsunami detection (scientific), characterization, and early-warning (hazards), cadastral surveying (governmental) and virtual reference networks (commercial). All of the applications depend on high-level operational and observational quality performance. It is imperative that network operators monitor these higher-rate real-time streams with appropriate performance metrics. Additionally, it is critical that data users understand the performance of any given station when designing their experiments or operationalized infrastructure.

In this presentation we investigate both the observational and operational pre-processed quality control metrics that influence station performance in this higher sample rates. Observational metrics are a measure of the receiver and antenna hardware performance within the station’s observational environment. Evidence suggests that some pre-processing metrics vary depending on sample rate for a given station. Operational quality control metrics capture station telemetry and network operations center performance, agnostic of receiver or antenna performance. Evidence suggests that current metrics, such as completeness, might not fully capture critical aspects of station real-time performance.

Presenting Author: Tim Dittmann

Authors