Efficient identification procedure for inversion processing [magnetospheric imaging]

Model extrapolation and data inversion are two important steps in control. Model extrapolation is a step to find control variables that are viable for the control process. Data inversion is a step to estimate the control variables from the sensor data. The assumptions and goals are different in these two steps. The model extrapolation step searches for the control variable that gives minimum errors between modelled and true measurements; the data inversion step searches for the values of the control variables that give minimum errors between the modelled and observed measurements. This paper discusses the control processing used in the magnetospheric image setting. The magnetosphere exists in the region of space that surrounds the Earth several hundred kilometres above the Earth´s surface, extends and is filled with magnetic field lines passing through the Earth´s surface. The magnetospheric image process is to estimate the ion-population within the field. The ion-population variables and the magnetospheric images have no easily modelled physical relationship. The gradient formula is hard to define, therefore gradient dependent optimization techniques are not applicable. Chase and Roelof (1995) use the Powell algorithm for magnetospheric model extrapolation. Unfortunately, the Powell method is a trial-and-error type of search algorithm and may break down in data inversion, due to high-noise sensors and multiple root problems. This paper shows that simultaneous perturbation stochastic approximation is more efficient than the Powell algorithm and performs well in the data inversion problems