基于卡方检验的GNSS观测值部分粗差抗差滤波算法

在进行抗差卡尔曼(Kalman)滤波过程中,为避免由于全球导航卫星系统(GNSS)观测值之间的相关性而导致粗差转移的问题,提出了一种基于卡方检验的GNSS观测值部分粗差抗差算法。首先,基于观测模型异常检验量,分析了观测值之间的相关性,并针对由于观测值之间的相关性所导致的粗差误判问题,提出了部分粗差抗差算法;根据假设检验理论,构造了滤波模型整体检验量,基于卡方检验判断整体模型是否存在异常,并给出了基于卡方检验的GNSS观测值部分粗差抗差算法整体流程框架;最后设计了两组实验,采用3种方法进行对比分析,以验证所提算法的性能。实验结果表明,所提算法极大地消弱了观测值之间相关性的影响,能准确的识别粗差位置,明显降低了粗差探测的误警率,保证了定位的鲁棒性。

Partial gross error robust filtering algorithm for GNSS observations based on chi square test

In the process of robust Kalman filtering, in order to avoid the problem of gross error transfer due to the correlation among Global Navigation Satellite System (GNSS) observations, a partial gross error robust filtering algorithm for GNSS observations based on chi square test is proposed. Firstly, the correlation among observations is analyzed based on the anomaly test of the observation model, and aiming at the problem of gross error misjudgment caused by the correlation among observations, a partial gross error robust filtering algorithm is proposed. According to the hypothesis testing theory, the overall test of the filtering model is constructed, which judges whether there exists an abnormality in the overall model based on chi square test, and the overall flow framework of the partial gross error robust filtering algorithm for GNSS observations based on chi square test is given. Finally, two sets of experiments are designed, and three methods are used for comparative analysis to verify the performance of the proposed algorithm. The experiment results show that the proposed algorithm greatly reduces the influence of correlation among observations, can accurately identify the location of gross errors, significantly reduces the false alarm rate of gross error detection and ensures the robustness of positioning.