Signal Processing

Low-cost inertial sensors are increasingly being used jointly in order to improve their navigation performance and this work provides a computationally feasible solution to solve these complex problems

This work combines the information from multiple signals to improve the estimation of stochastic model estimation for sensor calibration

This publication contributes to work in signal processing, with a focus reflected in its title: Applied Time Series Analysis With R.

This publication contributes to work in signal processing, with a focus reflected in its title: Use of a New Online Calibration Platform With Applications to Inertial Sensors.

This publication contributes to work in applied statistics, signal processing, with a focus reflected in its title: A Two-Step Computationally Efficient Procedure for IMU Classification and Calibration.

This publication contributes to work in signal processing, with a focus reflected in its title: An Optimal Virtual Inertial Sensor Framework Using Wavelet Cross Covariance.

This publication contributes to work in signal processing, with a focus reflected in its title: Improved Stochastic Modelling of Low-Cost GNSS Receivers Positioning Errors.

This paper introduces a robust, user-friendly R-based software platform using the generalized method of wavelet moments for efficient and statistically sound calibration of stochastic errors in inertial sensors, even with outlier-affected data

This publication contributes to work in signal processing, with a focus reflected in its title: A Computational Multivariate-Based Technique for Inertial Sensor Calibration.

This publication contributes to work in signal processing, with a focus reflected in its title: An Automatic Calibration Approach for the Stochastic Parameters of Inertial Sensors.