测绘学报 ›› 2024, Vol. 53 ›› Issue (1): 137-145.doi: 10.11947/j.AGCS.2024.20230101

• 大地测量学与导航 • 上一篇    下一篇

基于动态粒子群算法的ARAIM可用性优化方法

王尔申1,2, 孙薪蕙1, 曲萍萍1, 曾洪正3, 徐嵩1, 庞涛1   

  1. 1. 沈阳航空航天大学电子信息工程学院, 辽宁 沈阳 110136;
    2. 沈阳航空航天大学民用航空学院, 辽宁 沈阳 110136;
    3. 中国民用航空飞行学院民航飞行技术与飞行安全重点实验室, 四川 广汉 618307
  • 收稿日期:2023-04-20 修回日期:2023-11-22 发布日期:2024-02-06
  • 作者简介:王尔申(1980-),男,博士,教授,研究方向为北斗卫星导航、多源组合导航技术。E-mail:wanges2016@126.com
  • 基金资助:
    国家自然科学基金(62173237);嵩山实验室预研项目(YYJC062022017);卫星导航系统与装备技术国家重点实验室开放基金(CEPNT2022B04;CEPNT2022A01);省部共建动态测试技术国家重点实验室开放基金(2023-SYSJJ-04);民航卫星应用工程技术研究中心(RCCASA-2022003);辽宁省应用基础研究计划(2022020502-JH2/1013;2022JH2/101300150);民航飞行技术与飞行安全重点实验室开放基金(FZ2021KF15;FZ2021ZZ06;FZ2020KF09);沈阳市科技计划(22-322-3-34)

ARAIM availability optimization method based on dynamic particle swarm optimization algorithm

WANG Ershen1,2, SUN Xinhui1, QU Pingping1, ZENG Hongzheng3, XU Song1, PANG Tao1   

  1. 1. School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China;
    2. College of Civil Aviation, Shenyang Aerospace University, Shenyang 110136, China;
    3. Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan 618307, China
  • Received:2023-04-20 Revised:2023-11-22 Published:2024-02-06
  • Supported by:
    The National Natural Science Foundation of China (No. 62173237); The SongShan Laboratory Foundation (No. YYJC062022017); The Open Fund of State Key Laboratory of Satellite Navigation System and Equipment Technology (Nos. CEPNT2022B04; CEPNT2022A01); The Open Fund of State Key Laboratory of Dynamic Measurement Technology, North University of China (No. 2023-SYSJJ-04); The Center of Civil Aviation Satellite Application Engineering Technology (RCCASA-2022003); The Applied Basic Research Programs of Liaoning Province (Nos. 2022020502-JH2/1013; 2022JH2/101300150); The Open Fund of Key Laboratory of Flight Techniques and Flight Safety CAAC (Nos. FZ2021KF15; FZ2021ZZ06; FZ2020KF09); The Special Funds program of Shenyang Science and technology (No. 22-322-3-34)

摘要: 卫星导航的完好性监测技术对保障航空领域的导航安全至关重要。针对高级接收机自主完好性监测算法将完好性风险概率和连续性风险概率平均分配给所有可见卫星,导致垂直保护级较为保守,进而造成可用性降低的问题,本文提出了一种基于动态粒子群算法(dynamic particle swarm optimization,DPSO)的ARAIM可用性优化方法。通过优化风险概率分配过程,在完好性指标不变的情况下,可有效降低垂直保护级,提高了ARAIM算法的可用性。选取全球均匀分布的6个MGEX(multi-GNSS experiment)测站对所提方法进行验证,并分析了算法的全球可用性。同时,为验证该方法的有效性,在沈阳法库通航机场采集飞机全飞行阶段的卫星导航试验数据,对算法进行验证。静态数据与动态数据试验结果表明:采用基于DPSO算法的分配策略,降低了垂直保护级,提高了ARAIM可用性,全球范围内ARAIM可用性大于99.5%的覆盖比率由98.2%增加到99.7%。

关键词: GNSS, ARAIM, 可用性, 风险概率分配, 垂直保护级, 动态粒子群算法

Abstract: Integrity monitoring technology for satellite navigation is crucial to ensuring navigation safety in the aviation field. The advanced receiver autonomous integrity monitoring (ARAIM) algorithm distributes integrity risk probabilities and continuity risk probabilities evenly among all visible satellites, resulting in a conservative vertical protection level and reduced availability. This paper proposes an ARAIM availability optimization method based on dynamic particle swarm optimization (DPSO) algorithm. By optimizing the risk probability allocation process, the vertical protection level can be effectively reduced while the same integrity indicator, thus improving the availability of the ARAIM algorithm. The proposed method was verified and compared through experiments using six globally distributed MGEX (multi-GNSS experiment) stations, and the global availability of the algorithm was analyzed. In addition, to test the practicality of the method, satellite navigation test data for the entire flight phase of an aircraft was collected at the Shenyang Faku General Aviation Airport and the algorithm was experimentally validated.The experimental results from both static and dynamic data demonstrate that the adoption of allocation strategy based on DPSO algorithm can enhance the availability of ARAIM. The coverage rate of ARAIM availability worldwide, exceeding 99.5%, has increased from 98.2% to 99.7%.

Key words: GNSS, ARAIM, availability, risk probability allocation, vertical protection level, dynamic particle swarm optimization

中图分类号: