基于优选几何分组增强的假设检验RAIM方法

针对传统接收机自主完好性监测(RAIM)算法对15~50m级较小故障检测和排除率低,不易满足多测距故障场景应用问题,建立4种典型卫星分布几何结构模型,通过研究各模型对含偏差测距带来的定位误差与检测统计值间耦合性的影响,提出了一种基于几何优选分组增强的RAIM方法。该方法在假设检验方法基础上,利用分组间残差矢量建立新型检测统计量与门限值,通过检验子集一致性来检测和排除含有偏差的测距。仿真表明:该方法能够提升较小故障检测和排除率,增强接收机自主完好性性能水平,且支持联合导航系统单、多测距故障场景假设,有效降低定位估计误差,保证了定位解的精度。

Enhanced hypothesis test RAIM algorithm based on optimal geometry grouping

Aiming at problem that traditional receiver autonomous integrity monitoring （R_AIM） algorithms have low detection and exclusion rate for 15 -50 m level small faults, and are difficult to satisfy the application in multi- ranging faults scenarios, set up four typical models of geometry structure of satellite distribution, and through studing impact of model on the coupling between positioning error brought by ranging with deviation and detection statistics, present an enhanced R_AIM algorithm based on optimal geometry grouping. The novel algorithm, which is based on hypothesis test, establishes new detection statistics and thresholds by using the residual vector of groups, then detect and exclude ranging with deviation by testing the consistency of subsets. Simulations show that, the proposed algorithm can promote detection and exclusion rate for small faults, improve performance of RAIM, and support hypothesis of single-ranging fault or multi-ranging fault scenarios of combined navigation system effectively reduce estimated location error and guarantee precision of positioning solution.