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Fig. 2. UAV-based measurement instrument blocks along with the main uncertainty sources affecting them.
additional quantities coming from the UAV platform can influence
both the position and attitude determination as well
as the final measurement result, such as vibrations, turbulence
of the propellers, and magnetic fields generated by the motors.
Finally, the measurement results can be also affected by
environmental conditions such as temperature, light, and air
pressure.
It is then evident that in order to consider the UAV a proper
measurement instrument, a study about the identification
of the uncertainty sources and the other influence quantities
is needed, as well as the quantification of their effects on the
measurement results. Of course, for some uncertainty sources,
such as light conditions, motor vibrations, and propeller turbulences,
it is difficult to provide a model that describes their
effects on the measurement. In this case, specific experimental
setups must be designed for analyzing the measurement sensitivity
with respect to those influence quantities, and then it is
possible to estimate their contribution to the overall measurement
uncertainty. On the other hand, for all the uncertainty
sources that can be modelled in a closed-form, by applying the
law of propagation of uncertainty [9], a mathematical model
describing the uncertainty related to the specific measurement
can be obtained.
However, due to the complexity of assessing the measurement
uncertainty by a mathematical model, it is usually
evaluated in the literature by performing repeated measurements
after the deployment of the platform. For example, in
June 2021
[11], the authors propose a system embedded on UAVs for
measuring the water level in rivers and lakes and assessing
the uncertainties provided by several sensors by means of repeated
measurements. In this example, the UAV is equipped
with accurate lightweight sensors for measuring the range
to water surface. The ellipsoidal height of the water surface
is measured by subtracting the distance measured by the
ranging sensors from the vertical position retrieved by the
onboard GNSS receiver. The authors have tested three topologies
of ranging sensors embedded on UAV: radar-based,
sonar-based, and camera with laser-based. Several surveys of
the area and from the obtained measurements have been performed,
and the standard uncertainty associated to the level
measurements, provided by each type of ranging sensor, has
been evaluated. In particular, the sonar sensor has provided a
standard uncertainty value of 2 m higher than the others. This
is due to the fact that the measurements provided by sonar
are affected by the changing of the environmental conditions,
especially during high-speed maneuvers of the UAV. Furthermore,
the sonar sensor has a measurement range of 10 m lower
than the other methods. The camera with laser-based sensor is
affected by the environmental light conditions and exhibited a
standard uncertainty value of 1 m. The radar sensor has exhibited
the lower uncertainty value of 5 cm but it has high-power
consumption which requires a more powerful UAV. Again, the
in case of radar sensor, the main contribution to the uncertainty
is the measurement system based on GNSS. In this example, it
IEEE Instrumentation & Measurement Magazine
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