Multi-Hypotheses Ego-Tracking for Resilient Navigation
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
Keywords
Cite
@article{arxiv.2511.19770,
title = {Multi-Hypotheses Ego-Tracking for Resilient Navigation},
author = {Peter Iwer Hoedt Karstensen and Roberto Galeazzi},
journal= {arXiv preprint arXiv:2511.19770},
year = {2025}
}