A meta-classification-based approach for outlier identification in GNSS networks

In this contribution, we introduce a novel methodology for outlier identification in GNSS networks. The new method consists of a multilayer perceptron neural network-based meta-classifier. Meta-classifiers are classification models that use machine learning algorithms to integrate multiple base classifiers. A statistical testing procedure for outlier identification can be interpreted as a classifier. An observation is classified as an outlier or not based on the decision rule of the testing procedure. Here, we utilize the decision response of an observation being flagged as an outlier or not from the following procedures: iterative data-snooping (IDS), the minimum L1-norm (MinL1), Sequential Likelihood Ratio Tests for Multiple Outliers and the minimum L∞-norm (MinL∞). The binary classification whether the observation is or not an outlier from those procedures and their corresponding test statistics were employed as attributes to construct our meta-classifier. The experiments were conducted for GNSS networks with low ( \(r<0.5\) $r<0.5$ ), medium ( \(r=0.5\) $r=0.5$ ) and high redundancy ( \(r>0.5\) $r>0.5$ ). Results show that the proposed approach via meta-classification performs better than all base classifiers in low redundancy GNSS networks for a large margin. Its mean successful rate in outlier identification was always more than 11 percentage points higher than the best base classifier (MinL1, in this case). Moreover, it presented a higher performance in outlier identification with less collected baselines, which reduces the financial cost of the GNSS network, a key factor in surveying engineering projects. For medium and high redundancy networks, there was no significant improvement in the meta-classification performance against the best meta-classifier (IDS, in this case). As the results for low redundancy GNSS networks seem very promising, many potential future work suggestions were also made considering the reality of several countries.