  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.
