In order to improve the accuracy of navigation big data,it is necessary to identify the ab-normal data within it.In order to improve the accuracy and efficiency of the anomaly recognition for nonlinear navigation big data,an anomaly recognition method of nonlinear navigation big data is proposed based on a tensor decomposition.Firstly,tensor decomposition is carried out for non-linear navigation big data,and the sliding rectangular windows are used to partition data into sev-eral time window segments,then db4 wavelet is introduced for multi-scale decomposition of data within each window to obtain wavelet coefficients with different scales,and reconstructed wavelet coefficient tensors are used to replace missing data,for filling the missing value and improving data integrity;secondly,taking mutual information as a metric,a mutual information matrix for data is established,the elements in the mutual information matrix are normalized and centralized,and the data features are obtained through singular value decomposition;thirdly,random matrix theory is introduced to optimize feature selection,calculate the importance of navigation big data features,and obtain high-precision data features;finally,an isolated tree is established to provide anomaly scores for data features,and thereby completing anomaly recognition of nonlinear navigation big data.The experimental results show that the missing value filling accuracy of the proposed method remains above 0.9,the feature extraction coverage reaches 86.3％,the feature redundancy is less than 6.12％,the anomaly recognition accuracy G-mean value is higher than 60％,and the recogni-tion time is less than 8 s,which effectively improves the feature extraction accuracy,recognition accuracy,and recognition efficiency of nonlinear navigation big data.
Anomaly identificationTensor decompositionNonlinear navigation dataSingular value decompositionIsolation tree
NETL
NSTL
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