Parameter identification for state-space models with nuisanceparameters

The problem of identifying parameters in a dynamic model is considered based on the premise that certain parameters not being estimated are not known precisely. A procedure is described for accounting for these imprecisely known nuisance parameters when estimating the primary parameters of interest. The technique uses the asymptotic normality of the estimate together with the implicit function theorem to determine a correction to the estimate uncertainty evaluated from the Fisher information matrix. Efficient evaluation of the correction using Kalman filters is discussed and a numerical example for the X-22A aircraft is presented     

Total electron content measurements in ionospheric physics

With the advent of modern global networks of dual-frequency Global Positioning System (GPS), total electron content (TEC) measurements along slant paths connecting GPS receivers and satellites at 22,000 km have become the largest data set available to ionospheric scientists. The TEC can be calculated from the time and phase delay in the GPS signal using the GPS Toolkit, but an unknown bias will remain. In addition, UHF/VHF radio beacons on board low-Earth-orbiting satellites can also be used to measure the electron content. However, the TEC measurements are obtained by integrating TEC differences between slant paths, but also contain biases. It is often necessary to use data assimilative algorithms like the Ionospheric Data Assimilation Three-Dimensional (IDA3D), and to treat both GPS- and LEO-beacon TEC measurements as relative data in order to conduct ionospheric studies.