In previous works, methods have been demonstrated which infer refractivity from real-time sensor observations of the EM field, namely Refractivity From Radio (RFR) and Refractivity From Clutter (RFC) methods. Here we use a RFR method to infer atmospheric refractivity from data collected during the CASPER-East experiment, part of a Multi University Research Initiative (MURI) under funding from the Office of Naval Research.
One of the CASPER datasets consists of long-term power measurements aboard a moving ship received from signals of opportunity – digital television broadcasts and commercial FM radio. The spatial variation in received power is used to infer refractivity, first by reducing the problem space to a set of diagnostic parameter vectors, then by using an error-minimization approach to determine the best-fit from a library of precomputed RF loss functions. The performance of this RFR algorithm is evaluated for this dataset, and is validated against radiosonde refractivity observations during the same timeframe.
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