Roberto Molinari

Orcid: 0000-0001-8766-6902

According to our database1, Roberto Molinari authored at least 15 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2024
Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors.
IEEE Trans. Signal Process., 2024

2023
Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration.
IEEE Trans. Signal Process., 2023

2022
Scale-Wise Variance Minimization for Optimal Virtual Signals: An Approach for Redundant Gyroscopes.
IEEE Trans. Signal Process., 2022

2021
Perturbed M-Estimation: A Further Investigation of Robust Statistics for Differential Privacy.
CoRR, 2021

Granger-causal testing for irregularly sampled time series with application to nitrogen signalling in Arabidopsis.
Bioinform., 2021

2020
Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration.
IEEE Trans. Instrum. Meas., 2020

SWAG: A Wrapper Method for Sparse Learning.
CoRR, 2020

2019
Multivariate Signal Modeling With Applications to Inertial Sensor Calibration.
IEEE Trans. Signal Process., 2019

A Multisignal Wavelet Variance-Based Framework for Inertial Sensor Stochastic Error Modeling.
IEEE Trans. Instrum. Meas., 2019

2018
An optimal virtual inertial sensor framework using wavelet cross covariance.
Proceedings of the IEEE/ION Position, Location and Navigation Symposium, 2018

Improved stochastic modelling of low-cost GNSS receivers positioning errors.
Proceedings of the IEEE/ION Position, Location and Navigation Symposium, 2018

A two-step computationally efficient procedure for IMU classification and calibration.
Proceedings of the IEEE/ION Position, Location and Navigation Symposium, 2018

2017
A Study of the Allan Variance for Constant-Mean Nonstationary Processes.
IEEE Signal Process. Lett., 2017

2016
Wavelet-Based Improvements for Inertial Sensor Error Modeling.
IEEE Trans. Instrum. Meas., 2016

Theoretical Limitations of Allan Variance-based Regression for Time Series Model Estimation.
IEEE Signal Process. Lett., 2016


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