Abstract
The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.
Highlights
Integrity is a required feature for global navigation satellite system (GNSS) users
(1) We propose an RB receiver autonomous integrity monitoring (RAIM) method based on the MM estimator, which contains a least trimmed squares (LTS) estimator with a high breakdown point and an M estimator with high efficiency, making residuals more consistent with the actual ranging errors
We proposed an RB RAIM algorithm based on the MM
Summary
Integrity is a required feature for global navigation satellite system (GNSS) users. The snapshot receiver autonomous integrity monitoring (RAIM) algorithms based on consistency checks with redundant observations have been widely used especially in aviation [1]. Outliers participate in the process of parameter estimation and take the sum of the squares of pseudo-range residuals as the test statistic for outlier detection [2,3]. We propose solutions aiming at an RB RAIM detector based on a robust estimator. (1) We propose an RB RAIM method based on the MM estimator, which contains a least trimmed squares (LTS) estimator with a high breakdown point and an M estimator with high efficiency, making residuals more consistent with the actual ranging errors. The advantages of the RB RAIM detector are preserved and the ability to detect multiple simultaneous outliers is significantly improved.
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