针对地基增强系统(GBAS)中传统电离层异常检测方法无法同时兼顾检测精度与灵敏度的问题，通过构造单通道变步长最小均方(LMS)自适应滤波器以抑制伪码-载波偏离度高频噪声。单通道LMS自适应滤波器是在标准双通道LMS自适应滤波器的基础上，利用被检测信号短时相关性及其量化噪声的非相关性，构造一个采用被检测信号延时量作为参考输入的自适应滤波器，同时对Sigmoid函数进行改进，使得自适应滤波器在前期收敛速度快，且待滤波器收敛后保持较高稳定性。实验结果表明，在相同卫星仰角与电离层时间梯度值下，采用LMS自适应滤波器后电离层异常检测时间缩短，且当电离层时间梯度较小时，该方法也能够实现异常检测，验证了其有效性。"/>
Ionospheric Anomaly Detection for Ground Based Augmentation System Based on LMS Filtering" />
The traditional ionospheric anomaly detection method in ground-based augmentation system (GBAS) cannot take detection accuracy and sensitivity into account, so we apply the least mean square (LMS) adaptive filter with single-channel and variable step size to suppress the high-frequency noise of code-carrier divergence. On the basis of the standard two-channel LMS adaptive filter, we construct a single-channel LMS adaptive filter using delayed detected signal as reference input by utilizing the short-time correlation of the detected signal and the non-correlation of quantized noise. And at the same time, we improve the Sigmoid function to make the adaptive filter converge fast in the early stage and maintain high stability after the filter converges. The experimental results show that under the same satellite elevation and ionospheric time gradient, the detection time of ionospheric anomaly can be reduced by using the LMS adaptive filter, and the method can also achieve anomaly detection when the ionospheric time gradient is small, which verifies the effectiveness of the proposed algorithm."/>
基于LMS滤波的地基增强系统电离层异常检测
Abstract：The traditional ionospheric anomaly detection method in ground-based augmentation system (GBAS) cannot take detection accuracy and sensitivity into account, so we apply the least mean square (LMS) adaptive filter with single-channel and variable step size to suppress the high-frequency noise of code-carrier divergence. On the basis of the standard two-channel LMS adaptive filter, we construct a single-channel LMS adaptive filter using delayed detected signal as reference input by utilizing the short-time correlation of the detected signal and the non-correlation of quantized noise. And at the same time, we improve the Sigmoid function to make the adaptive filter converge fast in the early stage and maintain high stability after the filter converges. The experimental results show that under the same satellite elevation and ionospheric time gradient, the detection time of ionospheric anomaly can be reduced by using the LMS adaptive filter, and the method can also achieve anomaly detection when the ionospheric time gradient is small, which verifies the effectiveness of the proposed algorithm.
