UAV Signal Modulation Recognition Algorithm Based on Joint Features

The precise identification of drone modulation schemes serves as a fundamental basis for achieving intelligent drone recognition. To address the limitations of existing algorithms that overly rely on single features, resulting in low recognition rates and excessive model complexity, this paper proposes a drone signal modulation recognition algorithm based on joint features. The algorithm begins by employing Maximum Likelihood Estimation (MLE) to compensate for phase noise, mitigating its adverse effects on subsequent modulation recognition. Next, it combines the signal's time-frequency representation with IQ data derived from higher-order cumulants as joint features, which are then input into a recognition network composed of 2D convolutional layers (Conv2D) and Long Short-Term Memory (LSTM) networks. Additionally, parameter dynamic fixed-point quantization is applied to optimize the weights and biases of the model, reducing resource consumption during practical deployment. Experimental results demonstrate that when the Signal-to-Noise Ratio (SNR) exceeds 2 dB, the proposed algorithm achieves a recognition accuracy of up to 90% for nine common drone modulation schemes, substantially outperforming comparable models. After quantization, the recognition performance remains nearly unaffected, while computational resource requirements are greatly reduced, making the algorithm highly suitable for deployment in resource-constrained environments.