Abstract：Considering that the traditional harmonic model has difficulty accurately describing the nonlinear variation of GNSS coordinate time series, the signal and noise cannot be separated well, which further affects the gross error detection and noise estimation. This paper proposes an algorithm for gross error detection and noise component estimation based on singular spectrum analysis(SSA). The basic idea of the proposed algorithm is to separate the signal and noise with the SSA firstly, and then detect gross error in noise based on the inter-quartile range (IQR) criterion. Finally, we employ the least squares variance component estimation (LS_VCE) to quantitatively estimate each noise component. The analysis results show that the success rate of gross error detection of the new algorithm is higher than that of the traditional algorithm and the noise component estimation derived by the new algorithm is closer to the true value compared with the traditional algorithm.
TAO Guoqiang. Gross Error Detection and Noise Estimation of GNSS Coordinate Time Series Based on Singular Spectrum Analysis[J]. jgg, 2021, 41(12): 1223-1229.
