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arXiv · 2511.19770

Multi-Hypotheses Ego-Tracking for Resilient Navigation

Peter Iwer Hoedt Karstensen·Roberto Galeazzi
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Abstract

Autonomous robots relying on radio frequency (RF)-based localization such as global navigation satellite system (GNSS), ultra-wide band (UWB), and 5G integrated sensing and communication (ISAC) are vulnerable to spoofing and sensor manipulation. This paper presents a resilient navigation architecture that combines multi-hypothesis estimation with a Poisson binomial windowed-count detector for anomaly identification and isolation. A state machine coordinates transitions between operation, diagnosis, and mitigation, enabling adaptive response to adversarial conditions. When attacks are detected, trajectory re-planning based on differential flatness allows information-gathering maneuvers minimizing performance loss. Case studies demonstrate effective detection of biased sensors, maintenance of state estimation, and recovery of nominal operation under persistent spoofing attacks

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Peter Iwer Hoedt Karstensen, Roberto Galeazzi. 2025-11-24. Multi-Hypotheses Ego-Tracking for Resilient Navigation. https://arxiv.org/abs/2511.19770

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