The recent performance improvements of the Micro-Electro Mechanical Sensor, in terms of noise parameters, have allowed increasing their application fields such as consumer, structural health monitoring, and navigation applications thanks to their advantages in terms of cost, small dimension, low power consumption, and versatility. Furthermore, in aerospace and automotive sectors, the sensors adopted must strictly respect the noise parameters that classified the inertial sensors, i.e., bias instability and random walk, typically reached by tactical-grade sensors with higher cost. To make low-cost consumer-grade MEMS sensors suitable also for navigation applications, a redundant-IMU solution has been the subject of research. The authors proposed a suitable strategy to fuse the measurements obtained from all the IMU composed of an accelerometer and gyroscope mounted on a suitable and innovative 3D structure, based on the evaluation of the Allan variance and the use of weighted average.

Innovative Fusion Strategy for MEMS Redundant-IMU Exploiting Custom 3D Components

Silvestri, Alessia Teresa;
2022-01-01

Abstract

The recent performance improvements of the Micro-Electro Mechanical Sensor, in terms of noise parameters, have allowed increasing their application fields such as consumer, structural health monitoring, and navigation applications thanks to their advantages in terms of cost, small dimension, low power consumption, and versatility. Furthermore, in aerospace and automotive sectors, the sensors adopted must strictly respect the noise parameters that classified the inertial sensors, i.e., bias instability and random walk, typically reached by tactical-grade sensors with higher cost. To make low-cost consumer-grade MEMS sensors suitable also for navigation applications, a redundant-IMU solution has been the subject of research. The authors proposed a suitable strategy to fuse the measurements obtained from all the IMU composed of an accelerometer and gyroscope mounted on a suitable and innovative 3D structure, based on the evaluation of the Allan variance and the use of weighted average.
2022
978-1-6654-1077-9
Additive Manufacturing
Allan Variance
Data fusion
Multi-sensors
Redundant-IMU
Weighted average
Additive Manufacturing
Allan Variance
Data fusion
Multi-sensors
Redundant-IMU
Weighted average
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/36875
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