Zhao Lin, Yang Fuxin, Li Liang, Ding Jicheng, Zhao Yuxin
College of Automation, Harbin Engineering University, Harbin 150001, China.
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2016 May 26;16(6):763. doi: 10.3390/s16060763.
As one significant component of space environmental weather, the ionosphere has to be monitored using Global Positioning System (GPS) receivers for the Ground-Based Augmentation System (GBAS). This is because an ionospheric anomaly can pose a potential threat for GBAS to support safety-critical services. The traditional code-carrier divergence (CCD) methods, which have been widely used to detect the variants of the ionospheric gradient for GBAS, adopt a linear time-invariant low-pass filter to suppress the effect of high frequency noise on the detection of the ionospheric anomaly. However, there is a counterbalance between response time and estimation accuracy due to the fixed time constants. In order to release the limitation, a two-step approach (TSA) is proposed by integrating the cascaded linear time-invariant low-pass filters with the adaptive Kalman filter to detect the ionospheric gradient anomaly. The performance of the proposed method is tested by using simulated and real-world data, respectively. The simulation results show that the TSA can detect ionospheric gradient anomalies quickly, even when the noise is severer. Compared to the traditional CCD methods, the experiments from real-world GPS data indicate that the average estimation accuracy of the ionospheric gradient improves by more than 31.3%, and the average response time to the ionospheric gradient at a rate of 0.018 m/s improves by more than 59.3%, which demonstrates the ability of TSA to detect a small ionospheric gradient more rapidly.
作为空间环境天气的一个重要组成部分，电离层必须使用全球定位系统（GPS）接收机对地基增强系统（GBAS）进行监测。这是因为电离层异常可能对GBAS支持安全关键服务构成潜在威胁。传统的码载波偏差（CCD）方法已被广泛用于检测GBAS的电离层梯度变化，该方法采用线性时不变低通滤波器来抑制高频噪声对电离层异常检测的影响。然而，由于时间常数固定，响应时间和估计精度之间存在平衡。为了消除这种限制，提出了一种两步法（TSA），即将级联的线性时不变低通滤波器与自适应卡尔曼滤波器相结合来检测电离层梯度异常。分别使用模拟数据和实际数据对所提方法的性能进行了测试。仿真结果表明，即使在噪声更严重的情况下，TSA也能快速检测到电离层梯度异常。与传统的CCD方法相比，基于实际GPS数据的实验表明，电离层梯度的平均估计精度提高了31.3%以上，对电离层梯度变化率为0.018 m/s的平均响应时间提高了59.3%以上，这表明TSA能够更快速地检测到小的电离层梯度。
