Using TDCP Measurements in a Low-Cost PPP-IMU Hybridized Filter for Real-Time Applications

Abstract

Much of the focus in current positioning systems is on high accuracy, oriented by developing powerful and computationally heavy algorithms; however, this approach is not compatible with systems that require real-time capabilities. Furthermore, most Precise Point Positioning (PPP) algorithms use ambiguity estimation techniques in order to leverage the precision of carrier phase measurements. We developed a low-cost PPP algorithm fused with an Inertial Measurement Unit that uses Time Differenced Carrier Phase (TDCP) measurements which remove the need to resolve the ambiguities while still benefiting from the most accurate Global Navigation Satellite System (GNSS) observables. To maximize the accuracy and continuity of the positioning solution, we designed a tightly coupled Extended Kalman Filter that is capable of processing triple frequency code, Doppler, and carrier phase or TDCP measurements. We observed that the filter which uses TDCP measurements performs 52% better than the solution with solely the code and Doppler measurements in deep urban conditions and 5% in open-sky conditions while taking 66% less computation time (in MATLAB) than the filter with carrier phase measurements. The results demonstrate that TDCP measurements are a solid alternative to carrier phase measurements, especially in deep urban conditions, for any computationally limited applications while maintaining a high level of accuracy.