Pau Closas

About

Pau Closas is an Associate Professor at the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, since 2016. He received his MS and PhD in Electrical Engineering from the Universitat Politècnica de Catalunya (UPC) in 2003 and 2009, respectively. He also holds a MS degree in Advanced Mathematics and Mathematical Engineering from UPC since 2014. In 2003, he joined the Department of Signal Theory and Communications, UPC, as a Research Assistant. In 2008 he was Research Visitor at the Stony Brook University (SBU), NY, USA. In 2009 he joined the CTTC, where he was Senior Researcher and Head of the Statistical Inference for Communications and Positioning Department. His primary areas of interest include statistical and array signal processing, estimation and detection theory, stochastic filtering, robust statistics, and game theory, with applications to positioning systems, wireless communications, and mathematical biology.

He is the recipient of the EURASIP Best PhD Thesis Award 2014, the 9th Duran Farell Award for Technology Research, and the 2016 ION Early Achievement Award, in recognition to his contributions to navigation systems and signal processing fields. He served in the organization committees of flagship conferences, for instance, as Publications Chair of the IEEE SSP 2016 and the IEEE ICASSP 2020 conferences. He is also Guest Editor of special issues in the IEEE Trans. on Vehicular Technologies, the Proceedings of the IEEE, and the IEEE Sig. Proc. Magazine.

Selected Publications

• Elvira, Victor, Closas, Pau, Martino, Luca (2021). Importance Gaussian Quadrature. IEEE Transactions on Signal Processing, 69,474-488. 10.1109/TSP.2020.3045526
• Li, Haoqing, Medina, Daniel, Vila-Valls, Jordi, Closas, Pau (2021). Robust Variational-based Kalman Filter for Outlier Rejection with Correlated Measurements. IEEE Transactions on Signal Processing, 69,357-369. 10.1109/TSP.2020.3042944
• Medina, Daniel, Vilà-Valls, Jordi, Chaumette, Eric, Vincent, François, Closas, Pau (2021). Cramér-Rao bound for a mixture of real- and integer-valued parameter vectors and its application to the linear regression model. Signal Processing, 179,107792. 10.1016/J.SIGPRO.2020.107792
• Medina, Daniel, Li, Haoqing, Vilà-Valls, Jordi, Closas, Pau (2021). Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments. Sensors, 21(4),1250. 10.3390/S21041250
• Ortega, Lorenzo, Poulliat, Charly, Boucheret, Marie Laure, Aubault Roudier, Marion, Al-Bitar, Hanaa, Closas, Pau (2021). Low Complexity Robust Data Demodulation for GNSS. Sensors, 21(4),1341. 10.3390/S21041341
• Ortega, Lorenzo, Vilà-Valls, Jordi, Poulliat, Charly, Closas, Pau (2021). GNSS data demodulation over fading environments: Antipodal and M ‐ary CSK modulations. IET Radar Sonar and Navigation, 15(2),113-127. 10.1049/RSN2.12017
• Pastor, Francisco, Garcia-Gonzalez, Jorge, Gandarias, Juan M., Medina, Daniel, Closas, Pau, Garcia-Cerezo, Alfonso J., Gomez-de-Gabriel, Jesus M. (2021). Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information. IEEE Robotics and Automation Letters, 6(1),231-238. 10.1109/LRA.2020.3038377
• Borsoi, Ricardo A., Imbiriba, Tales, Closas, Pau, Bermudez, Jose Carlos M., Richard, Cedric (2020). Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing. IEEE Geoscience and Remote Sensing Letters, ,1-5. 10.1109/LGRS.2020.3025781
• Espluga, Lorenzo Ortega, Aubault-Roudier, Marion, Poulliat, Charly, Boucheret, Marie Laure, Al-Bitar, Hanaa, Closas, Pau (2020). LLR approximation for fading channels using a Bayesian approach. IEEE Communications Letters, 24(6),1244-1248. 10.1109/LCOMM.2020.2978832
• Vilà-Valls, Jordi, Linty, Nicola, Closas, Pau, Dovis, Fabio, Curran, James T. (2020). Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation. Navigation, 67(3),511-536. 10.1002/NAVI.379
• Vilà-Valls, Jordi, Vivet, Damien, Chaumette, Eric, Vincent, François, Closas, Pau (2020). Recursive Linearly Constrained Wiener Filter for Robust Multi-Channel Signal Processing. Signal Processing, 167,107291. 10.1016/J.SIGPRO.2019.107291
• J. Vilà-Valls, M. Navarro, P. Closas, M. Bertinelli Synchronization Challenges in Deep Space Communications, IEEE Aerospace and Electronic Magazine, 2019, 34(1), 16-27
• E. Arias-de-Reyna, P. Closas, D. Dardari, P.M. Djuric Crowd-based Learning of Spatial Fields for the IoT, IEEE Signal Processing Magazine, 35(5), 2018, 130-139
• D. Borio, H. Li, P. Closas, Huber’s Non-Linearity for GNSS Interference Mitigation, Sensors, 18(7), 2018, 2217
• P. Closas, A. Gusi-Amigó, Direct Position Estimation of GNSS Receivers, IEEE Signal Processing Magazine, 34(5), 2017, 72-84
• J. Vilà-Valls, P. Closas, J.T. Curran, Multi-frequency GNSS Robust Carrier Tracking for Ionospheric Scintillation Mitigation, Journal of Space Weather and Space Climate, 7, 2017, A26
• M.G. Amin, P. Closas, A. Broumandan, J.L. Volakis Vulnerabilities, Threats, and Authentication in Satellite- Based Navigation Systems [Scanning the Issue], Proceedings of the IEEE, 104(6), 2016, 1169-1173
• D. Dardari, P. Closas, P. Djuric, Indoor Tracking: Theory, Methods, and Technologies, IEEE Transactions on Vehicular Technology, 64(4), 2015, 1263-1278

Faculty

Jul 21, 2023

Advancing Worldwide Navigation Security with Distributed AI

ECE Associate Professor Pau Closas and Assistant Research Professor Tales Imbiriba, in collaboration with Tampere University and the University of Vaasa in Finland, was awarded a $1.4M NSF award for “Distributed AI for enhanced security in satellite-aided wireless navigation (RESILIENT).”

Faculty

May 24, 2023

Kostas Research Institute Receives $13M U.S. Army Contract for Wireless Research

The Kostas Research Institute (KRI) at Northeastern University has been awarded $13 million by the U.S. Army Research Laboratory for foundational research into Cognitive Distributed Sensing in Congested Radio Frequency Environments. KRI and five partner institutions will conduct innovative research to enable technologies in distributed machine learning, signal processing algorithms, and computational, sensing, and communication hardware infrastructure for distributed sensing and communication.

Faculty

May 08, 2023

Closas receives Best Paper in Track Award at IEEE/ION PLANS 2023

ECE Associate Professor Pau Closas received the Best Paper in Track Award at the 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS) for the work “Jammer Classification with Federated Learning*”, with electrical engineering students Peng Wu and Helena Calatrava, and Associate Research Scientist Tales Imbiriba. *P. Wu, H. Calatrava, T. Imbiriba, P. Closas, “Jammer Classification […]

Faculty

Apr 13, 2022

Closas Receives AESS Harry Rowe Mimno Award

ECE Assistant Professor Pau Closas received the Harry Rowe Mimno Award from the IEEE Aerospace and Electronic Systems Society (AESS) for his paper “State Estimation Methods in Navigation: Overview and Application.” This award recognizes and fosters excellence in clear communications of technical material of widespread interest to AESS members, and in doing so, to honor […]

In the Media

Jun 14, 2021

Research Roundup: Advanced High-Precision GNSS

ECE Assistant Professor Pau Closas’s research was featured in the GPS World article “Research Roundup: Advanced High-Precision GNSS“.

Faculty

Feb 25, 2021

Improving Flood Monitoring Methods

ECE Assistant Professor Pau Closas, Professor Deniz Erdogmus, CEE Professor Edward Beighley, and ECE Postdoc Tales Imbiriba were awarded a $98K grant from the National Geographic Society’s Committee for Research and Exploration for “Spaceborne Multispectral Image Fusion for Water Mapping and Flood Management”.

Faculty

May 07, 2020

$1M DARPA Grant for Signal Processing in Neural Networks for Wireless IoT

ECE Associate Professor Kaushik Chowdhury, Assistant Professor Pau Closas, Professor Deniz Erdogmus, Professor Tommaso Melodia, and Assistant Professor Yanzhi Wang received $1M funding from DARPA for their project titled Signal Processing in Neural Networks (SPiNN) for Wireless IoT.

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