Research on GNSS Time Series Noise Reduction Combining Principal Component Decomposition and Compound Evaluation Index

As a tool of adaptive signal decomposition, SSA can decompose GNSS time series into several SSA components, and select meaningful components to reconstruct, so as to reduce noise. In view of the fact that there is no general method to objectively determine the number of reconstruction layers, an adaptive SSA noise reduction method combining principal component decomposition and composite evaluation indicators is proposed: by combining the root mean square error and smoothness of the denoising signal Negatively correlated indicators are normalized, and then the coefficient of variation is used to determine the weight, and the two indicators are linearly combined to obtain the composite evaluation indicator T; then based on the principle component decomposition idea, the indicator T is combined to determine the number of reconstruction layers, T The smaller the value, the better the denoising effect and the better the number of corresponding reconstruction layers, so that the SSA method has adaptive denoising ability. This method no longer uses qualitative analysis, but uses quantitative analysis to accurately determine the optimal number of reconstruction layers. Through the analysis of simulation data and measured GNSS time series data, it is concluded that adaptive SSA can achieve the best ratio of the two negatively correlated indicators of noise reduction results in detail information and approximation information, and can be applied to GNSS time series under complex noise background.