Bayesian Echo Detection Applied to IMAGE Radio Plasma Imager Data

M. L. Rilee
S. A. Boardsen
J. L. Green
B. W. Reinisch


Bayesian echo detection uses probability to model ones' uncertainty about the 
characteristics of an echo signal within a given context.  A likelihood function encodes 
the understanding or knowledge we wish to bring to the analysis and is a probability 
density defined on observations, models, and defining parameters.  We have constructed a 
likelihood function for a simple echo model with additive noise for the Radio Plasma 
Imager (RPI) on IMAGE.  RPI is a low power radar that is currently obtaining remote 
sensing data about the density distribution of magnetospheric plasmas.  RPI's chief 
product is the plasmagram which shows received signal strength as a function of echo 
delay (range) and radio frequency of the radar pulses.  Radar echoes from important 
magnetospheric structures such as the magnetopause and the plasmaspause should show up as 
traces or ridges on the plasmagrams.  Various natural and artificial radio emissions will 
interfere and obscure echoes in the RPI data.  Our simple echo models attempt to recreate 
aspects of these traces, ridges, and noise, especially correlations in received echo 
strength that should occur across a number of pulse frequencies.  This analysis combines 
information obtained at a number of pulse frequencies so that echoes with low 
signal-to-noise ratios may be detected.  Furthermore, by appropriately restricting our 
likelihood's model and parameter domain, we are able to construct readily interpretable 
plasmagram-like maps that summarize the odds that an echo has been observed by RPI.  This 
analysis goes beyond more standard least-squares analyses to which our approach reduces in 
the limit of normal probability densities.

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Presented at the Fall American Geophysical Union Meeting, 
San Francisco, CA., December 15-19, 2000