GNSS高级接收机自主完好性监测随机模型精化

Stochastic model refinement of GNSS advanced receiver autonomous integrity monitoring

在众多涉及生命安全的应用领域,GNSS的用户端设备都必须具备完好性监测能力。高级接收机自主完好性监测(ARAIM)是完好性监测技术在民航领域的最新进展,有望扩展至多个应用领域。然而,ARAIM算法中的接收机噪声项通常使用航空无线电技术委员会(RTCA)推荐的常系数高度角模型,只能反映民航飞行环境下符合民航特定标准的GNSS接收机噪声特性。在众多应用场景中,接收机软、硬件和应用环境通常不符合ARAIM标准规范,且差异显著,若仍采用固化的常系数随机模型,难以保证ARAIM算法的有效性。为此,本文采用最小二乘方差分量估计(LS-VCE)自适应地构建ARAIM接收机噪声项随机模型,以扩展其算法的适用范围,并以GRACE-FO(GRACE Follow-On)的星载GNSS观测数据为例进行了有效性验证。试验结果表明,采用精化后的随机模型,一方面可提升解分离故障探测和排除的有效性,另一方面可有效降低水平与垂直保护级,提高完好性监测系统的可用性。

Global navigation satellite system(GNSS)receiver must have the capability of integrity monitoring in safety of life(SoL)applications.Advanced receiver autonomous integrity monitoring(ARAIM)which is expected to be extended to several application fields is the latest developments in the integrity monitoring of civil aviation,but the receiver-dependent term of stochastic model in ARAIM is usually established by an elevation-dependent model provided by radio technical committee for aeronautics(RTCA),which can only characterize the receiver noise of GNSS receiver of civil aviation.As a result,the performance of integrity monitoring would be adversely impacted.In this paper,the elevation-dependent model with adaptive coefficients to characterize the receiver-dependent errors is refined by the least square variance component estimation(LS-VCE)in order to extend the application scope of the ARAIM,and is verified by using the satellite-borne GNSS observation data of GRACE Follow-on(GRACE-FO)as an example.The results indicate that the overall performances of GNSS positioning and integrity monitoring are significantly improved by using the refined stochastic model.The ability of fault detection and exclusion is improved.Furthermore,protection level(PL)will decrease significantly,and so as to enhance the availability of the integrity monitoring system.