Bayesian analysis of linear phased-array radar
Andrew G Green and David J C MacKay
A number of methods have been developed to analyze the response of the
linear phased array radar. These perform remarkably well when the
number of sources is known, but in cases where a determination of this
number is required, problems are often encountered. These problems can be
resolved by a Bayesian approach.
Here, a linear phased-array consisting of equally spaced elements is
considered. Analytic expressions for the posterior probability
distribution over source positions and amplitudes, and the
corresponding Hessians are derived. These are integrated to give the
evidence for each model order.
Tests using model data showed that performance at the second level of
inference is critically determined by the accuracy of position
estimation. If adequate parameter optimization is available, the
Bayesian approach is demonstrated to work well, even in extreme
circumstances. A commonly employed method of source location, noise
subspace eigenanalysis of the correlation matrix, was tried and found
to be inadequate. A Newton-Raphson optimization was then used starting
from the
positions predicted by eigenanalysis.
postscript.
David MacKay's:
home page,
publications.