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20192026
most citedUWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation

60 citations · 106 across the 22 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

eess.SY2023★ 60 cited

UWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation

Hashim A. Hashim, Abdelrahman E. E. Eltoukhy, Kyriakos G. Vamvoudakis

This paper proposes a nonlinear stochastic complementary filter design for inertial navigation that takes advantage of a fusion of Ultra-wideband (UWB) and Inertial Measurement Uni…

eess.SY2023

An Observer-Based Reinforcement Learning Solution for Model-Following Problems

Mohammed I. Abouheaf, Kyriakos G. Vamvoudakis, Mohammad A. Mayyas +1

In this paper, a multi-objective model-following control problem is solved using an observer-based adaptive learning scheme. The overall goal is to regulate the model-following err…

eess.SY2023★ 1 cited

Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion

Hashim A. Hashim, Abdelrahman E. E. Eltoukhy, Kyriakos G. Vamvoudakis +1

Navigation in Global Positioning Systems (GPS)-denied environments requires robust estimators reliant on fusion of inertial sensors able to estimate rigid-body's orientation, posit…

cs.LG2023★ 9 cited

Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators

Styliani I. Kampezidou, Justin Romberg, Kyriakos G. Vamvoudakis +1

In this work, a novel Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers. This formul…

eess.SY2023★ 3 cited

An Online Model-Following Projection Mechanism Using Reinforcement Learning

Mohammed I. Abouheaf, Hashim A. Hashim, Mohammad A. Mayyas +1

In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control…