Abstract
The propagation of Global Positioning System (GPS) signals at low-elevation angles is significantly affected by a surface duct. In this paper, we present an improved algorithm known as NSSAGA, in which simulated annealing (SA) is combined with the non-dominated sorting genetic algorithm II (NSGA-II). Matched-field processing was used to remotely sense the refractivity structure by using the data of ground-based GPS phase delay and propagation loss from multiple antenna heights. The performance was checked by simulation data with and without noise. In comparison with NSGA-II, the new hybrid algorithm retrieved the refractivity structure more efficiently under various noise conditions. We then modified the objective function and found that matched-field processing is more effective than the conventional least-squares method for inferring the refractivity parameters. Comparing the inversion results and in situ sounding data suggests that the improved method presented herein can capture refractivity characteristics in realistic environments.
Highlights
An atmospheric duct is an atmospheric layer in which strong vertical gradients of refractivity determined by temperature and humidity affect the propagation of electromagnetic waves
The electromagnetic waves are trapped within the ducting layer with weak propagation loss and enhanced propagation range, usually known as atmospheric ducting [1,2]
Liao et al [35] used the non-dominated sorting genetic algorithm II (NSGA-II) to retrieve the atmospheric refractivity structure based on the ground-based Global Positioning System (GPS) phase delay and propagation loss
Summary
An atmospheric duct is an atmospheric layer in which strong vertical gradients of refractivity determined by temperature and humidity affect the propagation of electromagnetic waves. Turton et al [3] presented a classification of different radio-propagation conditions and corresponding atmospheric ducting effects. Wu et al [34] tried to combine the GPS phase delay and bending angle to retrieve refractivity profiles, but they neglected the fact that the observed phase delay and bending angles are not independent For this reason, Liao et al [35] used the non-dominated sorting genetic algorithm II (NSGA-II) to retrieve the atmospheric refractivity structure based on the ground-based GPS phase delay and propagation loss. The combination of ground-based GPS phase delay and propagation loss can infer refractivity parameters effectively, its performance is limited by the weak global search capability of NSGA-II.
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