The monitoring system is one of the indispensable components of the eLoran system, which can monitor the reliability and integrity of the eLoran system. In this paper, an eLoran monitoring system is designed based on the BPL time service system, and an integrity monitoring method based on the receiver time difference prediction model is designed according to the stability and accuracy of the receiver time difference. The deviation between the solved time difference and the predicted time difference is utilized to assist in integrity monitoring at the user’s end. And the test results show that the monitoring system can effectively determine the signal quality and system health of the eLoran system and provide early warning service for the system performance.

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.