6533b7d7fe1ef96bd126911c
RESEARCH PRODUCT
A hybrid observer for localization from noisy inertial data and sporadic position measurements
Filippo D’ippolitoGiovanni GarraffaAntonino SferlazzaLuca Zaccariansubject
Settore ING-INF/04 - AutomaticaControl and Systems EngineeringLocalization sampled-data observer sporadic measurements hybrid cascaded systemsAnalysisComputer Science Applicationsdescription
We propose an asymptotic position and speed observer for inertial navigation in the case where the position measurements are sporadic and affected by noise. We cast the problem in a hybrid dynamics framework where the continuous motion is affected by unknown continuous-time disturbances and the sporadic position measurements are affected by discrete-time noise. We show that the peculiar hybrid cascaded structure describing the estimation error dynamics is globally finite-gain exponentially ISS with gains depending intuitively on our tuning parameters. Experimental results, as well as the comparison with an Extended Kalman Filter (EKF), confirm the effectiveness of the proposed solution with an execution time two orders of magnitude faster and with a simplified observer tuning because our bounds are an explicit function of the observer tuning knobs
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2023-04-01 |