Gyro Sensor Acoustic Attack Detection for Unmanned Vehicles
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Solution Overview
Problem
Existing detection algorithms for gyro sensors in unmanned vehicles, particularly drones, are inadequate in identifying acoustic attacks, which can compromise the accuracy of angular velocity measurements and lead to control system failures, and existing methods either increase costs or are ineffective in dynamic environments.
Innovation Solution
A detection system using a Luenberger observer-based attack residual generation module, attack residual evaluation module, and attack diagnosis module to identify acoustic attacks on gyro sensors by generating residuals and differentials, calculating evaluation functions, and determining attacks through membership functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical isolation or resonance tuning methods are used to protect gyro sensors from acoustic attacks, then sensor reliability is improved, but device cost and complexity increase
Solution Approach 1:
The patent replaces physical/mechanical protection methods (physical isolation, resonance tuning) with a software-based detection algorithm. The system uses signal processing and pattern recognition to detect acoustic attacks on gyro sensors through computational analysis of sensor data, eliminating the need for complex physical shielding or hardware modifications.
Solution Approach 2:
The patent introduces an intermediary detection layer between the gyro sensor and the control system. This intermediary module monitors sensor outputs, identifies patterns indicative of acoustic attacks, and triggers appropriate responses, thereby protecting the system without requiring direct physical modification of the sensor itself.
2Reliability
If existing detection algorithms are applied to multiplexing sensor configurations, then sensor fault detection capability is improved, but adaptability to dynamic platforms deteriorates
Solution Approach 1:
The patent adapts the detection algorithm to dynamically changing environments by continuously monitoring sensor data streams and adjusting detection parameters in real-time. The system accounts for the dynamic nature of drone flight conditions, vibration patterns, and operational states, making the detection capability adaptable rather than static.
Solution Approach 2:
The patent develops a universal detection algorithm that can be applied across different sensor configurations (single sensor, multiplexing arrangements) and various dynamic platform types. The methodology is designed to be platform-agnostic, working effectively whether applied to drones, ground vehicles, or other mobile systems with inertial sensors.
3Ease of operation
If fixed threshold detection techniques are used for attack detection, then detection simplicity is improved, but detection precision under varying attack conditions deteriorates
Solution Approach 1:
The patent employs multiple detection thresholds and evaluation criteria rather than a single fixed threshold. The system uses a hierarchy of detection levels, combining simple threshold checks with more sophisticated pattern recognition methods, thereby achieving both operational simplicity and high detection precision across varying attack intensities.
Solution Approach 2:
The patent dynamically adjusts detection parameters including thresholds, time windows, and evaluation weights based on operating conditions, attack patterns observed, and sensor noise characteristics. This parameter adaptation allows the system to maintain high detection precision whether facing subtle or intense acoustic attacks, while preserving relative operational simplicity through automated parameter management.
Data Source
AI summary
Disclosed herein are an apparatus and method for detecting an attack on a gyro sensor of an unmanned vehicle. The apparatus for detecting an attack on a gyro sensor of an unmanned vehicle includes an attack residual generation module configured to generate a residual and a differential of the residual through fusion with sensor-based data, an attack residual evaluation module configured to calculate a value of an attack residual evaluation function for the generated residual and the generated residual differential, and an attack diagnosis module configured to determine whether an attack has been detected by comparing a certain threshold with the function value derived by the attack residual evaluation function.