灾害应急环境下智能终端高精度北斗增强定位方法

针对当前接收机自主完好性监测（receiver autonomous integrity monitoring，RAIM）中多个粗差难以快速有效识别的问题，在相关分析粗差检验理论的基础上，提出了一种基于观测数据集密度中心的多粗差探测RAIM算法。首先，利用QR检校法构建观测数据集；其次，使用改进的Mean Shift模型估计观测数据集密度中心；最后，对观测特征点与密度中心相关距离进行检验，实现多个粗差探测识别。利用实测数据仿真粗差，对粗差卫星和正常卫星与检校向量的相关距离差异进行分析，在存在单个、两个、3个粗差的情况下，粗差卫星和正常卫星与密度中心的相关距离平均差异分别为1.122 m和1.516 m、1.021 m和1.266 m、1.177 m和1.588 m；粗差卫星和正常卫星与残差向量的相关距离差异分别为0.639 m和1.142 m、0.497 m和0.510 m、0.108 m和0.198 m。结果表明，与基于残差向量的相关分析RAIM算法相比，在两个或多个粗差存在的情况下，基于密度中心相关分析的RAIM算法具有更优的粗差探测识别性能，可有效提高多系统定位可靠性。

GNSS Receiver Autonomous Integrity Monitoring (RAIM) Performance Analysis

  Objectives  With the development of the global navigation satellite system (GNSS), the number of visible satellites has increased, and the constellation configuration has been improved. While improving user observation information, multi-system GNSS positioning increases the risk of multiple gross errors, posing a threat to the integrity of the system and restricting the application of GNSS in complex environments. A common method, the receiver autonomous integrity monitoring (RAIM) to ensure the reliability of positioning based on single error, but the reliability of positioning would decline in the case of multiple gross errors. The problem of poor detection and recognition, based on the correlation analysis method of the post-test residual vector, the residual vector is affected by multiple gross errors, showing that the correlation with the observed feature vector of gross errors is weakened. The phenomenon makes gross error detection distortion. The aim of the study is to improving the accuracy of multiple gross errors detection by a new RAIM algorithm based on the density center of observed dataset.  Methods  This paper proposes a correlation analysis method based on the density center of the observation data set to realize the detection and identification of multiple gross errors. Firstly, we construct the observation dataset through the QR calibration method. Then, we estimate the density center by the improved Mean Shift model. Finally, we test the correlation distance between the observation feature points and the density center for detection and recognition of multiple gross errors was compared.  Result  The gross errors are simulated by the factual observation data, and the correlation distance of density center to gross error satellite and normal satellite, In the case of a single gross error, two gross errors, and three gross errors, the average difference correlation distance are 1.122 m and 1.516 m, 1.021 m and 1.266 m, 1.177 m and 1.588 m respectively. Compared the correlation distance of QR test vector to gross error satellite and normal satellite, the average difference correlation distance are 0.639 m and 1.142 m, 0.497 m and 0.510 m, 0.108 m and 0.198 m respectively.  Conclusions  The new RAIM algorithm overcomes the problem of gross error detection distortion caused by the reduced correlation between the calibration vector and the observation vector in the presence of multiple gross errors, which can effectively improve the reliability of multi-GNSS positioning.