Computationally efficient direct position estimation via low duty-cycling

Direct Position Estimation (DPE) is an unconventional GPS positioning technique that directly estimates the GPS navigation solution from the GPS raw signal. In our prior work, we have proposed and implemented a novel DPE receiver architecture that efficiently estimates and tracks a comprehensive underlying signal and navigation parameter set of three-dimensional (3D) position, clock bias, 3D velocity and clock drift without additional aiding information from an external source. To further reduce the computational load of DPE, we propose low duty-cycling of our DPE receiver architecture. Our duty-cycled DPE receiver algorithm consists of a computationally efficient DPE measurement update and a DPE time update that reduces the accumulation of signal tracking errors. Our DPE measurement update optimizes over navigation parameter subsets, combines and computes batch signal replica generation and correlation using Fast Fourier Transforms and estimates the navigation solution using a correlation-weighted mean. Our DPE time update iteratively predicts and updates the signal code phase and carrier doppler frequency parameters using updated satellite positions, velocities, clock biases and clock drifts calculated using the satellite broadcast ephemerides. We implemented our duty-cycled DPE receiver architecture using a commercial frontend and our software platform - PyGNSS. We conducted both static and dynamic open-sky experiments. From the signal tracking results of the static experiment, we demonstrate that our duty-cycled DPE receiver, with duty-cycling as low as 2%, shows similar performance to continuous DPE. From the positioning results of the dynamic experiment, we demonstrate that our duty-cycled DPE receiver, with duty-cycling as low as 2%, successfully tracks a moving vehicle; with an accuracy that outperforms continuous vector tracking under signal attenuation.