An ionospheric remote sensing method using an array of narrowband VLF transmitters and receivers

Very low frequency;VLF;Ionosphere;D region;Remote sensing;Waveguide;Narrowband;MSK;Neural network;Synthetic data

Gross, Nicholas ; Cohen, Morris Electrical and Computer Engineering Simon, Sven Davenport, Mark Steffes, Paul Golkowski, Mark ; Cohen, Morris

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Very low frequency;    VLF;    Ionosphere;    D region;    Remote sensing;    Waveguide;    Narrowband;    MSK;    Neural network;    Synthetic data;   

Others  :  https://smartech.gatech.edu/bitstream/1853/60763/1/GROSS-DISSERTATION-2018.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Narrowband 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 profile. VLF remote sensing experiments typically use a single narrowband VLF transmitter and receiver pair to determine a widely used two-parameter (known as waveguide parameters) exponential electron density profile. Inference of this profile with a single transmitter and receiver pair reveals temporal characteristics of the D-region, however, more than one transmitter and receiver pair are needed to deduce spatial D-region properties. This work expands upon single transmitter and receiver electron density profile inference methods to create a more generalized narrowband VLF remote sensing method that concurrently resolves the two-parameter electron density profile along an arbitrary number of transmitter and receiver paths. 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 waveguide parameters along all paths. The target function is approximated using an artificial neural network (ANN). Synthetic training data is generated using the US Navy's Long-Wavelength Propagation Capability (LWPC) program, which is then used to train the ANN. Real-world performance of the ANN is measured in two ways. First, ANN inferred waveguide parameters are compared to a variety of previously published narrowband VLF remote sensing experiments. Second, ANN inferred waveguide parameters are used in LWPC to predict narrowband VLF amplitude and carrier phase at a receiver that was withheld when performing the waveguide parameter inference. Results show the approximated target function performs well in capturing temporal and spatial characteristics of the D-region.