The Full Spectrum Inversion (FSI) method was developed in the beginning of 2002 in an effort to solve the multipath problem in radio occultation measurements. The physical ideas, which lead to the method, were that the occultation path could be considered as a synthetic aperture and the radio occultation Doppler frequency in a single path was a monotonic function of time. In star occultations, at optical wavelengths, the multipath problem is easily solved by having a lens in front of an array detector separating the beams in space. The lens is performing a spatial Fourier transform i.e., a plane wave is focused into a point displaced from the optical axis an amount given by the direction (the spatial frequency) of the plane wave. The analogy to this space processing method in time, is to have a “time lens”, which can separate multiple temporal frequencies occurring at the same time: Obviously this is what a temporal Fourier transform does. These ideas were implemented in 2001 and tested successfully on simulations of radio occultation signals, which had circular satellite orbits. However, for non-circular orbits the plain Fourier method turned out to give a not fully correct result and the work on the FSI emerged realizing that some preprocessing steps were necessary in order to eliminate the impact of non-radial orbits. This involves pre-calculation of phases, which, multiplied on the occultation signal, reduces the impact of the non-circular orbits on the resulting Fourier transform of the preprocessed signal. In 2003 the phasematching method was developed, where the impact of the non-circular orbits was totally solved, but with the cost that the processing could not be implemented with a fast Fourier transform. Both the FSI and the phasematching methods will be discussed in detail in this paper. The present development on the FSI method includes its practical implementation and making the method robust for mass processing of radio occultation signals. Filtering of signals in the FSI method is important and different filtering methods will be discussed in this paper.
展开▼
