To solve the problem of the sky-wave interference in radio positioning system operating in CDMA mode, an algorithm of sky-ground-wave separation is provided. Based on the MLE （maximum likelihood estimate）, and by es...To solve the problem of the sky-wave interference in radio positioning system operating in CDMA mode, an algorithm of sky-ground-wave separation is provided. Based on the MLE （maximum likelihood estimate）, and by estimating the amplitude and the phase of the sky-wave signal, the provided algorithm for separating skyground-wave is implemented. The mathematics model used for signal processing is established, and the possible solutions are provided. The structure and signal processing flow implementing the presented algorithm in the receiver are presented. A multi-channels signal searching idea is adopted, some of which process the sky-wave signal, and some of which process the ground-wave signal. Numerical analysis and simulation show that the proposed algorithm has higher accuracy, more rapid processing speed, and simpler implementation for the estimation of the sky-wave signal parameter, and can separate the sky-wave signal and ground-wave signal from the arrival combination signal effectively.展开更多
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...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。展开更多
文摘To solve the problem of the sky-wave interference in radio positioning system operating in CDMA mode, an algorithm of sky-ground-wave separation is provided. Based on the MLE （maximum likelihood estimate）, and by estimating the amplitude and the phase of the sky-wave signal, the provided algorithm for separating skyground-wave is implemented. The mathematics model used for signal processing is established, and the possible solutions are provided. The structure and signal processing flow implementing the presented algorithm in the receiver are presented. A multi-channels signal searching idea is adopted, some of which process the sky-wave signal, and some of which process the ground-wave signal. Numerical analysis and simulation show that the proposed algorithm has higher accuracy, more rapid processing speed, and simpler implementation for the estimation of the sky-wave signal parameter, and can separate the sky-wave signal and ground-wave signal from the arrival combination signal effectively.
文摘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。
