Multiple UAV Flight Control Signals Extraction Based on Optimized Local Maximum With FPGA Implementation

The illegal operation of unmanned aerial vehicles (UAVs) has raised significant public safety concerns, especially the challenge of identifying UAV operators in complex electromagnetic environments. This paper proposes an optimized local maximum method for extracting multiple UAV flight control signals. The proposed method employs a novel four-stage processing framework, enabling it to operate effectively under low signal-to-noise ratio (SNR), multi-signal interference, and spectral overlap conditions. First, the time-frequency transformation effectively distinguishes time-varying flight control signals from stationary white Gaussian noise using discrete short-time Fourier transform (STFT). Next, an improved multi-level local maximum method enhances the capture capability of weak signals when multiple signals coexist. Furthermore, a dynamic frequency block update mechanism groups flight control signal points into blocks, leaving image transmission signals and noise scattered. Finally, by calculating the kurtosis for each frequency band, irrelevant noise blocks are filtered to keep flight control signals with steep waveform. Experimental results demonstrate that the proposed method outperforms traditional methods in various scenarios. Moreover, through data flow reconstruction and hardware parallelism, the method is efficiently deployed on a field-programmable gate array (FPGA) platform. This not only accelerates processing speed, but also significantly reduces storage requirements and computational load, providing a feasible solution for practical engineering applications.