# Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles

## TL;DR

This paper introduces a real-time anomaly detection method for autonomous aerial vehicles using Recursive Least Squares, achieving high accuracy and providing a new dataset for fault detection research.

## Contribution

It presents a model-agnostic, real-time anomaly detection approach and releases a comprehensive fault detection dataset for autonomous aircraft.

## Key findings

- Precision of 88.23% in anomaly detection
- Recall of 88.23% in identifying faults
- 86.36% overall accuracy in flight tests

## Abstract

The recent increase in the use of aerial vehicles raises concerns about the safety and reliability of autonomous operations. There is a growing need for methods to monitor the status of these aircraft and report any faults and anomalies to the safety pilot or to the autopilot to deal with the emergency situation. In this paper, we present a real-time approach using the Recursive Least Squares method to detect anomalies in the behavior of an aircraft. The method models the relationship between correlated input-output pairs online and uses the model to detect anomalies. The result is an easy-to-deploy anomaly detection method that does not assume a specific aircraft model and can detect many types of faults and anomalies in a wide range of autonomous aircraft. The experiments on this method show a precision of 88.23%, recall of 88.23%, and 86.36% accuracy for over 22 flight tests. The other contribution is providing a new fault detection open dataset for autonomous aircraft, which contains complete data and the ground truth for 22 fixed-wing flights with eight different types of mid-flight actuator failures to help future fault detection research for aircraft. The source code and the dataset can be accessed from https://theairlab.org/fault-detection/.