Matthias Pätzold

Professor

Universitetet i Agder - Campus Grimstad

Norway

Research Interests

Explore related searches

Professors in Norway

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Matthias Pätzold is a Professor at Universitetet i Agder - Campus Grimstad in Norway. His research focuses on linear dynamic systems, mobile fading channels, and human activity recognition systems, with a particular emphasis on radar sensor applications. Recent publications include work on simulation-based frameworks for human activity recognition and performance analysis of diversity reception in radar systems.

Articles (16)

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Collaborators (2)

Stephan Sigg

Assistant Professor

Aalto University

FINLAND

View details

Andreu Català

Full Professor

Universitat Politècnica de Catalunya

SPAIN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Matthias Pätzold

Research Interests

Explore related searches

Contact this professor

About

Articles (16)

Global temporal observability of linear dynamic systems

Blind parameter identification of implicit differential equations using the collocation discretization and homotopy optimization methods

A Paradigm Shift From an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis

A Simulation-Based Framework for the Design of Human Activity Recognition Systems Using Radar Sensors

Performance Analysis of MRC Diversity Reception Over Double Hoyt Fading Channels

Polarimetric Modeling of Mobile Fading Channels

Orientation-Independent Human Activity Recognition Using Complementary Radio Frequency Sensing

Direction-Independent Human Activity Recognition Using a Distributed MIMO Radar System and Deep Learning

Human Motion Synthesis Using Trigonometric Splines

Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems

Interchannel Interference and Mitigation in Distributed MIMO RF Sensing

An RF-Based Positioning Method for Tracing a Cluster of Moving Scatterers in Non-Stationary Indoor Environments

A Trajectory-Driven 3D Channel Model for Human Activity Recognition

Channel Modeling and System Concepts for Future Terahertz Communications: Getting Ready for Advances Beyond 5G

Estimation of the Time-Variant Velocity of a Single Walking Person in Two-Dimensional Non-Stationary Indoor Environments Using Radio-Frequency Techniques

WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities

Collaborators (2)

Stephan Sigg

Andreu Català

Make your next move.