Deterministic Maximum Likelihood Algorithm for Sky-Ground Waves Recognition in ELoran Systems

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Abstract
It is crucial to recognize the sky and ground waves, i.e., to determine the delays of the waves, in enhanced long-range navigation (eLoran) systems. However, the performance of most existing algorithms sharply decreases when the delay differences $\Delta \tau$ between the sky and ground waves are small or the signal-to-noise ratios (SNRs) are low. In this paper, after transforming the originally time-domain sky-ground waves recognition problem to the frequency domain, we interestingly find that it can be effectively addressed by the deterministic maximum likelihood (DML). Thus, we design a DML-based sky-ground waves recognition algorithm. The verification results based on both the simulated and the realistic data show that the DML outperforms the four representative algorithms, including the IFFT, the FOC-MUSIC, the WRELAX, and the LM, in terms of the success rate in recognizing the groundwave, particularly in situations with small $\Delta \tau$ or low SNRs. Numerically, compared to the IFFT, the LM, and the FOC-MUSIC, the success rate achieved by the DML is improved by 67.11%, 66.67%, and 64.19% respectively, and the WRELAX fails to work, in a challenging situation of SNR $=0$ dB and $\Delta \tau =37\ \mu s$ .
Funding Information
  • National Natural Science Foundation of China (62471195)
  • Key Laboratory of Smart Earth (KF2023ZD01-04)
  • National Key Laboratory of Underwater Information and Control (2023-JCJQ-LB-030-10)

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