Pau Closas

Associate Professor

Northeastern University

United States

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About

Pau Closas is an Associate Professor at Northeastern University in the United States. His research areas include radar technology, machine learning applications in navigation and tracking, and advanced filtering techniques. He has authored and contributed to various articles focusing on optimizing radar placement, AI-assisted algorithms, and the classification of satellite imaging time-series.

Articles (20)

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Collaborators (7)

Edward Beighley

Professor

Northeastern University

UNITED STATES

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Murat Akcakaya

University of Pittsburgh

UNITED STATES

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Saikat Chatterjee

KTH Royal Institute of Technology

SWEDEN

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Peng Wu

Northeastern University

UNITED STATES

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Anubhab Ghosh

KTH Royal Institute of Technology

SWEDEN

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Jordi Vilà-Valls

Associate Professor

ISAE-SUPAERO

FRANCE

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Adrian Feiguin

Associate Professor

Northeastern University

UNITED STATES

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Pau Closas

Research Interests

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About

Articles (20)

Continuously Optimizing Radar Placement With Model-Predictive Path Integrals

Random Finite Set-Based Mitigation of Radar Range Deception Jamming for Single-Target Tracking

Artificial Intelligence-Aided Kalman Filters: AI-Augmented Designs for Kalman-Type Algorithms

From the Editor-in-Chief: Launching 2025

Robust Adaptive Nonlinear KF Under Hierarchically Gaussian Outliers

Recursive classification of satellite imaging time-series: An application to land cover mapping

Augmented Physics-Based Models for High-Order Markov Filtering

From the Editor-in-Chief

Augmented Physics-Based Machine Learning for Navigation and Tracking

Guest Editorial for the TAES Special Section on Machine Learning Methods for Aerial and Space Positioning and Navigation

Deep Learning of GNSS Acquisition

Online fusion of multi-resolution multispectral images with weakly supervised temporal dynamics

Dynamical Hyperspectral Unmixing With Variational Recurrent Neural Networks

Robust Interference Mitigation Techniques for Direct Position Estimation

Robust Linearly Constrained Kalman Filter for General Mismatched Linear State-Space Models

Sample generation for the spin-fermion model using neural networks

Federated Learning for Indoor Localization via Model Reliability With Dropout

Deep Neural Network Correlators for GNSS Multipath Mitigation

Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing

Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing

Collaborators (7)

Edward Beighley

Murat Akcakaya

Saikat Chatterjee

Peng Wu

Anubhab Ghosh

Jordi Vilà-Valls

Adrian Feiguin

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