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20192021
most citedNonlinear State Estimation for Inertial Navigation Systems With Intermittent Measurements

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

eess.SY2021

Nonlinear Attitude Estimation Using Intermittent Linear Velocity and Vector Measurements

Miaomiao Wang, Abdelhamid Tayebi

This paper investigates the problem of continuous attitude estimation on using continuous angular velocity and linear acceleration measurements as well as intermittent line…

math.OC2021

Nonlinear Observers Design for Vision-Aided Inertial Navigation Systems

Miaomiao Wang, Soulaimane Berkane, Abdelhamid Tayebi

This paper deals with the simultaneous estimation of the attitude, position and linear velocity for vision-aided inertial navigation systems. We propose a nonlinear observer on $SO…

math.OC2020

Hybrid Feedback for Global Tracking on Matrix Lie Groups and

Miaomiao Wang, Abdelhamid Tayebi

We introduce a new hybrid control strategy, which is conceptually different from the commonly used synergistic hybrid approaches, to efficiently deal with the problem of the undesi…

math.OC2020

Observers Design for Inertial Navigation Systems: A Brief Tutorial

Miaomiao Wang, Abdelhamid Tayebi

The design of navigation observers able to simultaneously estimate the position, linear velocity and orientation of a vehicle in a three-dimensional space is crucial in many roboti…

math.OC20201 cited

Nonlinear State Estimation for Inertial Navigation Systems With Intermittent Measurements

Miaomiao Wang, Abdelhamid Tayebi

This paper considers the problem of simultaneous estimation of the attitude, position and linear velocity for vehicles navigating in a three-dimensional space. We propose two types…

math.OC2019

Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements

Miaomiao Wang, Abdelhamid Tayebi

This paper considers the problem of attitude, position and linear velocity estimation for rigid body systems relying on landmark measurements. We propose two hybrid nonlinear obser…