Near real-time monitoring of the ionosphere using dual frequency GPS data in a Kalman filter approach

Kalman filter estimation of plasmaspheric total electron content using GPS

“…If the satellite is not in view, then its corresponding component in y k and corresponding row in H k are set to zero. Following Anghel et al [2008], we specify the M × M process noise covariance matrix Q and the N × N measurement covariance matrix R as follows: In the specification of Q as above, we obtain the best results when only the diagonal elements corresponding to the ionospheric state variables are nonzero since the ionosphere generally varies on much shorter time scales than either the plasmasphere or the instrumental biases. Alternatively, one may specify the process noise as a Gauss‐Markov process, which may provide improved control of the time scales over which the ionosphere and plasmasphere are permitted to vary in the Kalman filter solution.…”

“…We note that a number of alternative formulations of this algorithm are possible. For instance, Anghel et al [2008] describe how WinTEC can be used to process the TEC from a network of GPS receivers simultaneously using a modified form of . Anghel et al [2009] explore the use of higher‐order terms (quadratic and cubic) when fitting the vertical TEC in the ionosphere.…”

“…[10] We note that a number of alternative formulations of this algorithm are possible. For instance, Anghel et al [2008] describe how WinTEC can be used to process the TEC from a network of GPS receivers simultaneously using a modified form of equations (6) -(9). Anghel et al…”

“…Because of the dispersive nature of the ionosphere and plasmasphere, a dual‐frequency GPS receiver can measure the total electron content (TEC) along the signal path, provided that instrumental biases associated with the receiver and satellites can be accurately estimated and removed. A number of techniques have been developed for this purpose [ Sardon et al , 1994; Rideout and Coster , 2006; Komjathy and Langley , 1996; Carrano and Groves , 2006; Anghel et al , 2008]. These techniques employ a thin slab approximation for the ionosphere and exploit the fact that to first order, the measured slant TEC depends systematically on the satellite elevation, while the instrumental biases do not.…”

“…The algorithm we present is an extension of a Kalman filter technique for GPS TEC estimation called WinTEC, which was developed by the Space Weather Prediction Center [ Anghel et al , 2008]. In the WinTEC approach, biased measurements of slant TEC are modeled according to their elevation‐dependent paths through a thin ionospheric slab.…”