Optimization of covert spoofing parameters for loosely coupled GNSS/INS systems based on improved genetic algorithm

Area of Science:

• Navigation Systems Engineering
• Cybersecurity for Autonomous Systems
• Aerospace Engineering

Background:

• Unmanned aerial vehicles (UAVs) are increasingly vulnerable to navigation spoofing.
• Modern UAVs utilize Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) and innovation detection to counter spoofing.
• Existing spoofing methods struggle against advanced anti-spoofing measures.

Purpose of the Study:

• To develop an innovative covert spoofing algorithm for GNSS/INS navigation systems.
• To overcome the limitations of current spoofing techniques against UAVs equipped with advanced navigation systems.
• To enable successful spoofing without triggering detection alarms.

Main Methods:

• Formulating covert spoofing as a constrained single-objective optimization problem.
• Applying genetic algorithms (GAs) to optimize spoofing parameters for the first time in this context.
• Enhancing GA processes (selection, crossover, mutation) for dynamic parameter adjustment.

Main Results:

• The proposed algorithm successfully spoofed a target UAV without detection alarms.
• The covert spoofing algorithm effectively manipulated a loosely coupled GNSS/INS system to a designated location.
• Simulation results validated the algorithm's efficacy against advanced anti-spoofing measures.

Conclusions:

• The novel GA-based covert spoofing algorithm is effective against sophisticated GNSS/INS navigation systems.
• This approach provides a viable method for covertly managing UAV operations.
• The research contributes to understanding and mitigating navigation spoofing vulnerabilities in UAVs.