基于天波重构技术的eLORAN信号周期识别算法

针对增强型罗兰(eLORAN)系统在信号处理中的核心问题——周期识别，该文提出一种应对强天波干扰及低信噪比(SNR)等恶劣环境的联合算法。该方法首先在改进窗函数的基础上利用频谱相除技术估计信号特征参数，并根据大数理论的思想实现了天地波识别；其次，提出天地波时延差及幅度比的自适应2阶网格搜索匹配算法，在节省计算量的同时准确重构并去除天波；最后利用伪地波信号准确实现周期识别。仿真结果分析表明，该算法能够成功地克服现有技术中的一些弊端，实现小时延差及大强度天波干扰下的天波识别及分离，同时结合频谱相除技术的稳定性极大提高周期识别的正确率，进而为后续解调解码等流程提供保障。

A Cycle Identification Algorithm for enhanced LOng RAnge Navigation Signal Based on Skywave Reconstruction Technology

To solve the core problem in signal processing of the enhanced LOng RAnge Navigation (eLORAN) system—cycle-identification, a joint algorithm for harsh condition such as high intensity skywave interference and low Signal-Noise-Ratio (SNR) is proposed in this paper. Firstly, based on the improved window function in this method, the characteristic parameters of signal are estimated by spectral division technology, and then the identification of ground and sky wave is realized according to the thought of large number theory. Secondly, in order to reconstruct accurately and remove the skywave while saving the computation, a two-stage adaptive searching and matching algorithm of the characteristic parameters is proposed. Finally, the cycle-identification is realized accurately by the output pseudo-groundwave. The analysis of simulation results show that the proposed algorithm can successfully overcome some disadvantages of the prior art, and realize the recognition and separation of skywave in the environment of low time-delay and high level skywave. In addition, the accuracy rate of cycle-identification is greatly improved combining with the stability of spectral division technology, so as to provide a guarantee for the subsequent demodulation and decoding processes.