摘要
ELoran系统作为Loran-C的增强型导航系统,其信号的高精度周期识别对于实现精确定位和授时至关重要。然而,在实际应用中,由于天波干扰和噪声的影响,传统的信号周期识别方法容易出现误差,导致定位精度下降。为了解决这一问题,本文首先采用自适应窗宽的频谱相除方法来获取信号的天地波时延和幅值信息。该方法通过调整窗宽,能够在不同信噪比和天地波强度条件下,同时获得较为精确的时延和幅值信息。其次引入粒子群优化算法对快速傅里叶逆变换(Inverse Fast Fourier Transform,IFFT)时延估计结果进行校正。粒子群优化算法通过模拟粒子群体的动态行为,能够有效地搜索到最优的时延估计值,从而显著降低估计误差。解决了传统的频谱相除方法在低信噪比情况下容易受到噪声的影响,导致时延估计误差较大的问题。仿真实验结果表明,该算法在不同信噪比、时延差和幅值比条件下均能准确估计地波时延,误差小于0.5μs,显著优于传统的IFFT和多重信号分类(Multiple Signal Classification,MUSIC)算法。最后利用天波抑制算法降低了天波幅值,在减小了对地波影响的同时让IFFT处于最佳性能区域。仿真结果显示,在信噪比大于0 dB的情况下,该算法的地波时延估计准确率均能保持在90%以上。经过分析,本文算法不仅实现了在强天波、低信噪比条件下天地波分离,同时解决了传统方法的误差问题,为ELoran信号的高精度定位和解码提供了新的思路和方法。
The enhanced Loran(ELoran)system,an advancement of the Loran-C navigation system,plays a critical role in high-precision positioning and timing through accurate signal period recognition.However,in practical applications,conventional signal period recognition methods are susceptible to errors caused by skywave interference and noise,which degrade positioning accuracy.To address this limitation,this study first employed a spectrum division approach with an adaptive window width to extract delay and amplitude information for both skywave and groundwave components.By adjusting the window width,the proposed method can obtain relatively accurate delay and amplitude information under varying signal-to-noise ratio(SNR)and skywave-groundwave strength conditions.The particle swarm optimization(PSO)algorithm is employed to refine the IFFT time-delay estimation results.By simulating the dynamic behavior of a particle swarm,the PSO algorithm effectively searches for the optimal delay estimation,thereby significantly reducing estimation errors.This approach addresses the issue of significant delay estimation errors in traditional spectrum division methods under low SNR conditions,where susceptibility to noise is pronounced.Simulation results demonstrate that the proposed algorithm accurately estimates groundwave delay across a range of SNR levels,delay differences,and amplitude ratios,with an error less than 0.5μs,significantly outperforming traditional IFFT and MUSIC algorithms.Finally,a skywave suppression algorithm was implemented to attenuate the amplitude of the skywave,thereby minimizing its interference with the groundwave signal and enhancing the overall performance of the IFFT.Simulation results indicate that the algorithm achieves over 90%accuracy in groundwave time-delay estimation for SNRs above 0 dB.Further analysis reveals that the algorithm not only enables the separation of sky wave and ground wave under the conditions of strong sky wave and low signal-to-noise ratio but also addresses the error limitations inherent in traditional methods.This advancement offers a novel approach for high-precision positioning and decoding of ELoran signals.
作者
邢哲
徐颖
曾凌川
XING Zhe;XU Ying;ZENG Lingchuan(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;School of Automation,Beijing Information Science&Technology University,Beijing 100192,China)
出处
《信号处理》
北大核心
2025年第7期1229-1240,共12页
Journal of Signal Processing
基金
中国科学院青年创新促进会(E33314010D)。
关键词
增强型罗兰导航
频谱相除
天地波识别
粒子群优化
ELoran
spectral division
sky-ground wave identification
particle swarm optimization
作者简介
邢哲,男,2000年生,北京人。北京信息科技大学硕士研究生,主要研究方向为长波无线电导航技术。E-mail:2022020358@bistu.edu.cn;徐颖,女,1983年生,四川眉山人。中国科学院空天信息创新研究院研究员,博士,主要研究方向为卫星导航定位技术。E-mail:xuying@aircas.ac.cn;通信作者:曾凌川,男,1988年生,福建莆田人。中国科学院空天信息创新研究院高工,博士,主要研究方向为卫星导航技术。E-mail:zenglingchuan@aoe.ac.cn。
