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RECEIVER AUTONOMOUS INTEGRITY MONITORING VIA SINGLE KALMAN FILTER

机译：通过单个卡尔曼滤波器进行接收器自主完整性监控

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Receiver autonomous integrity monitoring (RAIM) on global positioning system (GPS) is a crucial issue in air navigation because satellite failures may result in serious deviations of aircrafts from their intended flight paths. The purpose of RAIM is to detect the presence of unacceptably large position error and, further, to exclude the error source. Thus, the GPS navigation is allowed to continue. In this paper, a new approach based on a single Kalman filter is proposed by authors to speed up failure detection and reduce the incorrect exclusion rate (IER). The kinematic behavior of the failure vector can be described as a well-known position-velocity-acceleration (PVA) model. Then, by applying the Kalman filter, the updated state vector, v, and the updated covariance matrix, C, can be obtained. Furthermore, a test statistic can be defined as scalar norm of the v normalized by C, and it is chi-square distributed. Therefore, the detection threshold under a given false alarm rate can be obtained directly. After a satellite failure is detected, the updated state vector, v, can also be used to exclude the failed satellite. Simulation results show that, in comparison with the conventional parity space method, the best improvement rate for failure detection is 19% under the ramp-type failure with slope = 5 m/s, and 16% under step-type failure with step = 27 m. As for the case of failure exclusion, the maximal reduction of the IER is 7.0% under the ramp-type failure with slope = 0.2 m/s, and 9.3% under step-type failure with step = 20 m. In conclusion, the Kalman filter can reduce the IER in excluding the failed satellite.
机译：全球定位系统（GPS）上的接收机自主完整性监控（RAIM）是空中航行中的关键问题，因为卫星故障可能会导致飞机严重偏离其预定飞行路线。 RAIM的目的是检测不可接受的大位置误差的存在，并进一步排除误差源。因此，允许GPS导航继续。在本文中，作者提出了一种基于单个卡尔曼滤波器的新方法，以加快故障检测速度并降低错误排除率（IER）。可以将故障向量的运动学特性描述为众所周知的位置-速度-加速度（PVA）模型。然后，通过应用卡尔曼滤波器，可以获得更新后的状态向量v和更新后的协方差矩阵C。此外，检验统计量可以定义为用C归一化的v的标量范数，并且它是卡方分布的。因此，可以直接获得给定虚警率下的检测阈值。在检测到卫星故障后，更新的状态向量v也可以用于排除故障卫星。仿真结果表明，与常规奇偶空间方法相比，在斜坡= 5 m / s的斜坡型故障下，故障检测的最佳改进率为19％，在台阶= 27的阶梯型故障下，故障检测的最佳改进率为16％。米对于排除故障的情况，在坡度为0.2 m / s的斜坡型故障下，IER的最大降低为7.0％，在阶跃为20 m的阶梯型故障下，IER的最大降低为9.3％。总而言之，卡尔曼滤波器可以减少排除故障卫星的IER。

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