基于预滤波器和两级AIME的GNSS/INS超紧组合慢变故障检测

Detection of slowly growing faults of GNSS/INS ultra-tight integration based on prefilters and two-stage AIME

针对包含预滤波器的GNSS/INS非相干超紧组合架构,提出了一种基于预滤波器的两级AIME慢变故障检测方法。该方法首先基于预滤波器构建第1级AIME检测,并设计了关于第1级AIME检测统计量的检测量Kalman滤波器。在发生慢变故障时,第1级AIME故障检测统计量存在递增趋势。对于检测量Kalman滤波器而言,这种递增趋势也可视作一种慢变故障。以检测量Kalman滤波器为基础,构建了第2级AIME检测算法,以达到减小故障检测时间的目的。在单星和两星伪距慢变故障场景下,进行了仿真与对比分析。仿真结果表明,所提方法能够正确识别故障卫星并且可以显著减小慢变故障的检测时间。对于小变化率的慢变故障,所提方法在检测时间上的优势更加明显。

A two-stage Autonomous Integrity Monitored Extrapolation(AIME)method is proposed based on channel prefilters to detect the Slowly Growing Faults(SGFs)in the non-coherent ultra-tight integration architecture of the Global Navigation Satellite System(GNSS)and the Inertial Navigation System(INS).First,the first-stage AIME detection is constructed based on channel prefilters.Then,based on the test statistics of the first-stage AIME,a Test Statistic Kalman Filter(TSKF)is developed.In the presence of SGFs,the test statistics of the first-stage AIME show a gradually increasing trend.This increasing trend can also be regarded as a slowly growing fault for the TSKF.Based on the TSKF,the second-stage AIME detection algorithm is developed to reduce the fault detection time.Computer simulations and comparative analyses were carried out under the scenarios of single-satellite and two-satellite SGFs introduced in the pseudorange.Simulation results demonstrate that the proposed method can significantly reduce the detection time of SGFs.For the SGFs with a small growing rate,the proposed method has more obvious advantages in detection time.