An MLP-Based Demodulation Method for eLoran Ninth-Pulse Signals

As a crucial long-range positioning, navigation, and timing (PNT) system, eLoran will utilize the ninth pulse to broadcast differential data. However, conventional demodulation methods are ill-suited to the unique modulation characteristics of the ninth pulse and suffer from poor noise resistance, necessitating a more efficient demodulation solution. To address this, this paper proposes a lightweight Multilayer Perceptron (MLP)-based demodulation framework designed explicitly for the eLoran ninth pulse. The approach begins with a preprocessing stage that extracts a 1600-point key segment from each received frame, which is then fed into a compact MLP architecture with a 1600-dimensional input layer, a 512-neuron hidden layer, and a 32-class output layer trained using the Adam optimizer. Experimental results demonstrate that the proposed model achieves 99.97% accuracy on the validation set and maintains over 90% demodulation accuracy even at an SNR of −10 dB, whereas the improved EPD algorithm yields only about 70% demodulation accuracy. Notably, although the improved EPD algorithm itself exhibits a clear performance advantage over the basic correlation method and the peak-position detection method—both of which still present non-zero error rates even at an SNR of 20 dB—it remains significantly inferior to the proposed MLP-based scheme in the low-SNR regime. In addition, CNN-based and LSTM-based demodulation models show very poor performance under severe noise conditions, with symbol error rates rising to around 0.8 at −10 dB, despite being able to reach an error-free state when the SNR increases to approximately 2 dB. By adopting an end-to-end learning strategy, the method effectively avoids performance degradation caused by inter-module error propagation, while combining high precision with strong noise immunity. These features meet the requirements for real-time differential data reception and highlight the promising engineering potential of neural-network-based demodulation for high-reliability PNT applications in complex electromagnetic environments.