HopSAC: Frequency Hopping Parameter Estimation Based on Random Sample Consensus for Counter-Unmanned Aircraft Systems

Small unmanned aircraft systems (UAS), commonly known as drones and widely used in recreational and commercial applications, have caused alarming concerns of public safety and homeland security due to frequently reported unauthorized UAS incidents in recent years. To effectively disable potential threats from frequency hopping drones and controllers, the counter attack of Counter-UAS (CUAS) systems typically require parameter estimation of the frequency hopping signals with high precision and low complexity for real-time responses. Therefore, a model parameter estimation method to meet all these requirements becomes a challenge for CUAS systems. In this paper, a novel hopping parameter estimation method based on random sample consensus called HopSAC is proposed to conquer this challenge. Given a small set of samples, HopSAC estimates the parameters of linear frequency hopping sequence and achieves high multiple target detection performance with low implementation complexity that can be realized in real time. Simulation results show that the proposed HopSAC significantly outperforms linear Least Squares method in achieving exceptional accuracy of model parameter estimation under the impact of gross errors, timing errors, and multiple UAS targets.