Improving GPS Precision and Processing Time using Parallel and Reduced-Length Wiener Filters

TL;DR

This paper explores the use of Wiener filters, including parallel and reduced-length variants, to improve GPS accuracy and reduce processing time, offering a cost-effective alternative to traditional GPS/INS systems.

Contribution

It introduces parallel and reduced-length Wiener filter implementations for GPS signal processing, demonstrating improved efficiency and comparable precision to Kalman filters and neural networks.

Findings

Wiener filter achieves comparable accuracy to Kalman filter and neural network.

Parallel and reduced-length Wiener filters significantly reduce processing time.

Sampling frequency analysis enables matching processing times across methods.

Abstract

Increasing GPS precision at low cost has always been a challenge for the manufacturers of the GPS receivers. This paper proposes the use of a Wiener filter for increasing precision in substitution of traditional GPS/INS fusion systems, which require expensive inertial systems. In this paper, we first implement and compare three GPS signal processing schemes: a Kalman filter, a neural network and a Wiener filter and compare them in terms of precision and the processing time. To further reduce the processing time of Wiener filter, we propose parallel and reduced-length implementations. Finally, we calculate the sampling frequency that would be required in every Wiener scheme in order to obtain the same total processing time as the Kalman filter and the neural network.