Aerospace and Electronic Systems Magazine September 2017 - 14

Graceful Degradation and Recovery from GNSS Interruptions
filters starts growing. This causes the weights associated with that
filter to become smaller and thus play less of roll in the final state
estimate used by the UAV flight controller.
While the filtering approach was shown to be effective in
dealing with GNSS denial, like any engineering solution, this architecture represents a comprise among many competing requirements. As such, it has some drawbacks when compared with the
traditional aerospace approach of hardware redundancy for dealing
with failed sensors. We note two of them here, because they are the
focus on ongoing work. First, the computational overhead associated with this approach can be high, especially if a large number of
parallel filters are used. By limiting the number of inputs to each
blending filter to two, the computational overhead was made manageable. While increasing the number of inputs to each blender
provides flexibility, the computations associated with maintaining knowledge of correlations between parallel filters will lead
to computational burdens that cannot be handled by the typical
small processors found on most small UAV FCSs. Thus, methods
for streamlining the computations so that they are not computer
resource intensive is important. One promising approach is to use
covariance intersection or bounded covariance inflation filters [9].
This allows placing of bounds on the correlations that can exist
between parallel filters and thus obviates the need for the computation to propagate interfilter correlation.
The second issue that needs further exploration is the way in
which blending weights are computed. A key input to calculating the weights (as well as the triggers for the fault detection and
isolation algorithm) are the covariances estimated by the various
parallel filters. This can be problematic if the covariances used by
the parallel filter do not match the true statistics. This can lead to
turning off a particular filter when no faults were present or, worse,
retaining a filter with a failed sensor in the blended solution. Thus,
integrating the decentralized filtering with fault detection and isolation schemes is essential. In addition, quantifying its ability to
deal with loss of signal in terms of metrics such as false alarm rates
(i.e., reducing the magnitude of blending weights when there was
no need to do so) or missed detection rates (keeping a blending
weight at a large value when it should be reduced) is important in
safety-critical UAV applications.

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Table of Contents for the Digital Edition of Aerospace and Electronic Systems Magazine September 2017