Latency Compensation for Differential GPS

Abstract

Differential GPS (DGPS) has the potential to reduce spherical position error standard deviation to approximately 1 m. However, two practical issues affect the accuracy that can be achieved in real-time applications: long sampling periods and data latency. Low-bandwidth communications channels can result in differential corrections with sampling periods greater than 5 s. A time delay (latency) is inherent between the generation of pseudorange corrections and their application by the user. For real-time applications (e.g., autonomous vehicle navigation), low DGPS correction update rates and data latency are a fundamental performance limitation. This paper analyzes the modeling of differential corrections, uses the resulting optimal models for differential correction latency compensation, and analyzes the resulting accuracy by comparison with the first-order polynomial predictors recommended in the RTCM-104 standard. The comparison shows that, as long as the base station produces appropriate coefficients for the polynomial predictor, there is no significant accuracy improvement achieved by an optimal (i.e., Kalman filter-based) predictor over the RTCM-recommended polynomial predictor.