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Abstract:
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Non-GPS tracking systems often use fused information from sensors such as gyros and
accelerometer, either stand-alone or as parts of Inertial Measurement Units (IMUs), to estimate
changes in the users heading. Since gyros measure rate of rotation, they require, among other
mathematical processing, that their signals be numerically integrated to produce the desired
heading information. The numeric integration has a tendency to cause errors due to drift. Drift
is produced when small, near-constant deviations from the correct signal are integrated with
respect to time. The highly undesirable result of drift is that the error of the computed heading
increases continuously and without bound. One can conceptually view drift as being comprised
of two components: a slowly changing, near-DC component, called bias instability, and a highfrequency
noise component with an average of zero (called Angle Random Walk ARW). The
high-frequency component creates only relatively small errors in the computation of heading
from the gyros rate of turn measurements, since its average is about zero. In the context of
this paper, we are therefore concerned only with the near-DC component of drift. Gyros are
also sensitive to changes in temperature, and certain gyros are sensitive to linear accelerations.
In our application, these two effects also produce errors that can be treated as having near-
DC, slow changing components, as drift does. Our proposed drift reduction method counteracts
all near-DC errors regardless of whether they are caused by the physical phenomena of drift,
temperature sensitivity, or sensitivity to accelerations. For that reason, throughout this paper we
lump all three of these near-DC error sources together and call them collectively drift before
constructing a heuristic model to reduce error based on idiosyncratic behavior. |