VLF Remote Sensing of the D Region Ionosphere Using Neural Networks

Abstract

AbstractNarrowband very low frequency (VLF) remote sensing has proven to be a useful tool for characterizing the ionosphere's D region (60‐ to 90‐km altitude) electron density. This work expands upon single transmitter‐receiver pair electron density profile inference methods to create a more generalized narrowband VLF remote sensing method that concurrently resolves a two‐parameter electron density profile for an arbitrary number of transmitter‐receiver pairs. A target function is constructed to take in a single time step of narrowband amplitude and phase observations from an arbitrary number of transmitter and receiver combinations and return the inferred average waveguide parameters along all paths. The target function is approximated using an artificial neural network (ANN). Synthetic training data are generated using the U.S. Navy's Long‐Wavelength Propagation Capability program, which is then used to train the ANN. Performance of the ANN with real‐world data is measured in two ways. First, ANN inferred average waveguide parameters are compared to a variety of previously published narrowband VLF remote sensing experiments. Second, ANN inferred average waveguide parameters are used in Long‐Wavelength Propagation Capability to predict narrowband VLF amplitude and carrier phase at a receiver that was withheld when performing the average waveguide parameter inference. Results show the approximated target function performs well in capturing temporal and spatial characteristics of the D region.