Application of particle swarm optimization method to incoherent scatter radar measurement of ionosphere parameters

Simultaneous derivation of multiple ionospheric parameters from the incoherent scatter power spectra in the F-1 region is difficult because the spectra have only subtle differences for different combinations of parameters. In this study, we apply a particle swarm optimizer (PSO) to incoherent scatter power spectrum fitting and compare it to the commonly used least squares fitting (LSF) technique. The PSO method is found to outperform the LSF method in practically all scenarios using simulated data. The PSO method offers the advantages of not being sensitive to initial assumptions and allowing physical constraints to be easily built into the model. When simultaneously fitting for molecular ion fraction (f(m)), ion temperature (T-i), and ratio of ion to electron temperature ((T)), (T) is largely stable. The uncertainty between f(m) and T-i can be described as a quadratic relationship. The significance of this result is that T-i can be retroactively corrected for data archived many years ago where the assumption of f(m) may not be accurate, and the original power spectra are unavailable. In our discussion, we emphasize the fitting for f(m), which is a difficult parameter to obtain. PSO method is often successful in obtaining f(m), whereas LSF fails. We apply both PSO and LSF to actual observations made by the Arecibo incoherent scatter radar. The results show that PSO method is a viable method to simultaneously determine ion and electron temperatures and molecular ion fraction when the last is greater than 0.3.