Multi-scale Kalman filters algorithm for GPS common-view observation data based on correlation structure of discrete wavelet coefficients

Abstract

Global positioning system (GPS) common-view observation data were processed by using the multi-scale Kalman algorithm based on a correlative structure of the discrete wavelet coefficients. Suppose that the GPS common-view observation data has the 1/ƒ fractal characteristic, the algorithm of wavelet transform was used to estimate the Hurst parameter H of GPS clock difference data. When 0<H<1, the 1/&fonf; fractal characteristic of the GPS clock difference data is a Gaussian zero-mean and non-stationary stochastic process. Thus, the discrete wavelet coefficients can be discussed in the process of estimating multi-scale Kalman coefficients. Furthermore, the discrete clock difference can be estimated. The single-channel and multi-channel common-view observation data were processed respectively. Comparisons were made between the results obtained and the Circular T data. Simulation results show that the algorithm discussed in this paper is both feasible and effective.