1 research outputs found
Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial
Inertial navigation systems (INS) are widely used in almost any operational
environment, including aviation, marine, and land vehicles. Inertial
measurements from accelerometers and gyroscopes allow the INS to estimate
position, velocity, and orientation of its host vehicle. However, as inherent
sensor measurement errors propagate into the state estimates, accuracy degrades
over time. To mitigate the resulting drift in state estimates, different
approaches of parametric and state estimation are proposed to compensate for
undesirable errors, using frequency-domain filtering or external information
fusion. Another approach uses multiple inertial sensors, a field with rapid
growth potential and applications. The increased sampling of the observed
phenomenon results in the improvement of several key factors such as signal
accuracy, frequency resolution, noise rejection, and higher redundancy. This
study offers an analysis tutorial of basic multiple inertial operation, with a
new perspective on the error relationship to time, and number of sensors. To
that end, a stationary and levelled sensors array is taken, and its robustness
against the instrumental errors is analyzed. Subsequently, the hypothesized
analytical model is compared with the experimental results, and the level of
agreement between them is thoroughly discussed. Ultimately, our results
showcase the vast potential of employing multiple sensors, as we observe
improvements spanning from the signal level to the navigation states. This
tutorial is suitable for both newcomers and people experienced with multiple
inertial sensors