A GNSS PPP integrity monitoring algorithm based on ARKF and MHSS

IF 2.8 3区地球科学 Q2 ASTRONOMY & ASTROPHYSICS

Advances in Space Research Pub Date : 2025-03-05 DOI:10.1016/j.asr.2025.03.004

Ershen Wang , Tengli Yu , Xinhui Sun , Pingping Qu , Tingyu Chen , Xuebao Hong , Song Xu , Zexin Liu

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引用次数: 0

Abstract

Satellite navigation integrity monitoring techniques are essential to ensure navigation safety in aviation. The availability of precise point positioning (PPP) integrity monitoring is challenged by the introduction of hazardously misleading information (HMI) influenced by receiver measurement noise as well as complex application scenarios. Aiming at this problem, this study proposed a PPP integrity monitoring algorithm based on adaptive robust Kalman filter and multiple hypothesis solution separation (ARKF-MHSS). It fully integrates the robustness and adaptivity of ARKF with the fault detection and processing capability of MHSS, and introduces an equivalent covariance matrix to reduce the influence of observation noise on the position solution results, which effectively improves the availability of PPP integrity monitoring in complex environments. The effectiveness of the ARKF-MHSS algorithm in airborne PPP scenarios is fully verified by conducting static evaluations based on International GNSS Service (IGS) stations and dynamic evaluations based on general aviation flight tests. The experimental results show that in the static test, the stricter the alert limit (AL), the more significant the improvement of the ARKF-MHSS algorithm. When the HAL is set to 3 m and 2.5 m, the average improvement in availability is 0.43 % and 13.13 %, respectively. In the flight test, the algorithm can effectively meet the positioning performance requirements of the LPV-200 (Localizer Performance with Vertical guidance) approach phase, and the availability reaches 100 %. In the Category I precision approach (CAT-I) phase (VAL = 10 m), the availability reaches 98.2 %.

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基于ARKF和MHSS的GNSS PPP完整性监测算法

卫星导航完整性监测技术是保障航空导航安全的重要手段。受接收机测量噪声影响的危险误导信息（HMI）的引入以及复杂的应用场景，对精确点定位（PPP）完整性监测的可用性提出了挑战。针对这一问题，本文提出了一种基于自适应鲁棒卡尔曼滤波和多假设解分离（ARKF-MHSS）的PPP完整性监测算法。将ARKF的鲁棒性和自适应能力与MHSS的故障检测和处理能力充分结合，引入等效协方差矩阵，降低观测噪声对位置解算结果的影响，有效提高了复杂环境下PPP完整性监测的可用性。通过基于国际GNSS服务（IGS）站的静态评估和基于通用航空飞行试验的动态评估，充分验证了ARKF-MHSS算法在机载PPP场景下的有效性。实验结果表明，在静态测试中，警戒限（AL）越严格，ARKF-MHSS算法的改进越显著。当HAL设置为3 m和2.5 m时，可用性的平均提高幅度分别为0.43%和13.13%。在飞行试验中，该算法能有效满足LPV-200 （Localizer performance with Vertical制导）进近阶段的定位性能要求，可用性达到100%。在第一类精度方法（CAT-I）阶段（VAL = 10 m），可用性达到98.2%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Space Research 地学天文-地球科学综合

CiteScore

5.20

自引率

11.50%

发文量

800

审稿时长

5.8 months

期刊介绍： The COSPAR publication Advances in Space Research (ASR) is an open journal covering all areas of space research including: space studies of the Earth''s surface, meteorology, climate, the Earth-Moon system, planets and small bodies of the solar system, upper atmospheres, ionospheres and magnetospheres of the Earth and planets including reference atmospheres, space plasmas in the solar system, astrophysics from space, materials sciences in space, fundamental physics in space, space debris, space weather, Earth observations of space phenomena, etc. NB: Please note that manuscripts related to life sciences as related to space are no more accepted for submission to Advances in Space Research. Such manuscripts should now be submitted to the new COSPAR Journal Life Sciences in Space Research (LSSR). All submissions are reviewed by two scientists in the field. COSPAR is an interdisciplinary scientific organization concerned with the progress of space research on an international scale. Operating under the rules of ICSU, COSPAR ignores political considerations and considers all questions solely from the scientific viewpoint.