Study on GPS common-view observation data with multiscale Kalman filter based on correlation structure of the discrete wavelet coefficients

In this paper, we pay our attention to the multiscale kalman algorithm based on correlation structure of the discrete wavelet coefficients for the restoration of the GPS common-view observation data. Based on the hypotheses of that the GPS common-view observation data being pretreatment possess of 1/f fractal characteristics. In this condition, we estimate the Hurst parameter of GPS clock difference data based upon the wavelet transform. When 0 < H < 1, the GPS clock difference data is taken for as a Gaussian Zero-mean nonstationary stochastic process which can be considered having the 1/f fractal characteristics. So, we can talk about the correlation structure of the discrete wavelet coefficients. During the course of the estimation of the GPS common-view data with the multiscale kalman bank, we process the single-channel and multi-channel common-view observation data, respectively. Comparisons between which results and circular T demonstrate our algorithm's feasibility and effectiveness.