An Zero-Point Drift Suppression Method for eLoran Signal Based on a Segmented Inaction Algorithm

Research on interference suppression technology for enhanced long-range navigation (eLoran) signals is crucial for enhancing receiver performance. To address the zero-point drift phenomenon in eLoran signals during adaptive filtering, we propose a segmented inaction algorithm based on normal time–frequency transform (NTFT), which is designed for challenging environments, such as low signal-to-noise ratio (SNR) and complex noise conditions. The algorithm splits the 20 kHz frequency band of the eLoran signal into 200 equal sub-bands, then applies the inaction algorithm sequentially to each sub-band, which exhibits strong noise resistance and high robustness. It is regarded as a pre-filter of the adaptive filter, ensuring a cleaner input signal for subsequent processing. Simulation results indicate that, when processing low-SNR eLoran signals affected by multi-frequency narrow-band interference and band-limited Gaussian noise, the combined algorithm significantly improves root mean square error (RMSE) by 33.3% and relative root mean square error (R-RMSE) by 39.1% compared to the single VSS-LMS method. Additionally, it compensates for zero-point drift (the deviation observed in the time series between the positive zero-crossing point of the third period of the reconstructed signal and that of the original signal) by 79.3% and maintains third-week forward over-zero error at a very low level. The effectiveness of the combined algorithm was further validated through actual measurement experiments.