Helena Calatrava, Northeastern University
Statistical Signal Processing for Resilient Positioning and Tracking
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Schedule At a Glance (EST)
Web: https://scholar.google.com/citations?user=SDEdzV8AAAAJ&hl=es
Abstract: Positioning and tracking are key enablers of a wide range of applications that require reliable and accurate location information. However, these technologies are vulnerable to both intentional attacks and unintentional interference, motivating the need for resilient solutions. This talk presents probabilistic, uncertainty-aware approaches to two fundamental challenges that threaten situational awareness in today’s hostile environments. First, we study resilient satellite-based navigation under infrastructure outages. We propose cooperative positioning strategies in large-scale real-time kinematic networks that achieve centimeter-level accuracy despite missing or mixed-quality reference data, and analyze performance as a function of network size, geometry, and robustness to outliers from jamming attacks and multipath. Second, we counter deception jamming in radar-based localization using multi-target tracking (MTT) frameworks based on random finite set theory. In particular, we focus on range gate pull-off attacks, which generate adversarial radar returns intended to deceive the tracker into following false targets. By exploiting attack characteristics within the MTT algorithm, we significantly reduce spoofed track persistence. Together, these contributions provide a path toward resilient navigation and sensing systems through probabilistic and Bayesian methods.
Bio: Helena Calatrava received the B.S. and M.S. degrees in Electrical Engineering from the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, in 2020 and 2022, respectively. She is currently a Ph.D. candidate in Electrical and Computer Engineering at Northeastern University's Information Processing Laboratory, Boston, MA, USA. Her research focuses on statistical signal processing, robust estimation, and multitarget tracking algorithms to improve resilience in satellite-based navigation and radar-based localization systems, with an emphasis on cooperative and distributed architectures. During her internship at Albora Technologies she explored lightweight interference mitigation techniques. She is the co-recipient of a Best Track Paper Award at IEEE/ION PLANS 2023 for work on federated learning for GNSS jamming signal classification.