Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems

TL;DR

This paper introduces an attack-aware multi-sensor integration algorithm for autonomous vehicle navigation that detects cyberattacks quickly and accurately using an extended Kalman filter and statistical residual analysis.

Contribution

It presents a novel fault detection method combining Kalman filtering with statistical analysis for cyberattack detection in multi-sensor autonomous navigation systems.

Findings

Effective detection of cyberattacks with low false alarm rates

Quick detection capabilities demonstrated in INS/GNSS integration simulation

Robust residuals constructed for dynamic system noise conditions

Abstract

In this paper, we propose a fault detection and isolation based attack-aware multi-sensor integration algorithm for the detection of cyberattacks in autonomous vehicle navigation systems. The proposed algorithm uses an extended Kalman filter to construct robust residuals in the presence of noise, and then uses a parametric statistical tool to identify cyberattacks. The parametric statistical tool is based on the residuals constructed by the measurement history rather than one measurement at a time in the properties of discrete-time signals and dynamic systems. This approach allows the proposed multi-sensor integration algorithm to provide quick detection and low false alarm rates for applications in dynamic systems. An example of INS/GNSS integration of autonomous navigation systems is presented to validate the proposed algorithm by using a software-in-the-loop simulation.