Multi-Hypotheses Ego-Tracking for Resilient Navigation

TL;DR

This paper introduces a resilient navigation system for autonomous robots that detects and mitigates RF spoofing attacks using multi-hypothesis estimation, anomaly detection, and adaptive re-planning to maintain accurate localization.

Contribution

It presents a novel architecture combining multi-hypothesis estimation with anomaly detection and adaptive re-planning for resilient RF-based robot navigation.

Findings

Effective detection of biased sensors under spoofing attacks

Maintains accurate state estimation during adversarial conditions

Recovers nominal operation through trajectory re-planning

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