Algorithms for Eliminating User Position Biases Caused by Satellite Constellation Changes or Differential Signal Gain or Loss in Kalman Filter and Weighted Least Squares Solutions
2009
Loss or gain of positioning satellites and loss or gain of a
differential correction signal are events that lead to
undesirable GPS user position biases. These biases appear
in the form of instantaneous jumps in a Weighted Least
Squares WLS solution and drifts in a Kalman Filter KF
solution. As a result, in both solutions, GPS positioning
becomes problematic in various types of applications. For
instance, on an agricultural field, when the user is trying
to maintain long, parallel and straight swaths that are
tightly close to one another, these position biases occur
causing the vehicle to divert from its straight line path.
Although, a bias might lead to a more accurate position in
the absolute sense, in this work, what is sought-after is
relative accuracy to a selected initial epoch comprising its
satellite constellation and differential signal status which are considered the reference of truth. All gains or losses
of constellation satellites or the differential signal that
occur after the initial epoch are considered problematic
and the biases they create must be eliminated. To deal
with this task, in the case of WLS, a position bias filter
PBF was implemented in the position domain. The PBF
simply tracks and calculates the WLS position biases
caused by every event and accumulates them in a position
bias vector that is subtracted from the WLS solution’s
position vector. Unfortunately a PBF cannot be used with
a KF solution because the KF diffuses the instantaneous
position jumps, encountered in WLS, into drifts over the
course of several epochs, making it difficult to assess the
biases in the position domain during these epochs.
However, since pseudorange measurements are part of the
input to a KF, another bias filter, called PseudoRange
Bias Filter PRBF, that does the position bias elimination
in the pseudorange domain, is introduced. The PRBF-KF
combination demonstrates the ability to reap the benefits
of both: position bias filtering and Kalman filtering in the
same solution. This paper presents the two algorithms for
position bias filtering: the PBF for the WLS solution and
the PRBF for the KF solution, under all possible scenarios
of satellite constellation and differential signal changes.
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