### ASA 127th Meeting M.I.T. 1994 June 6-10

## 4aUW12. Passive synthetic aperture processing as a Kalman filter problem.

**Edmund J. Sullivan
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*Naval Undersea Warfare Ctr., Newport, RI 02841
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**James V. Candy
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*Lawrence Livermore Natl. Lab., Univ. of California, Livermore, CA 94550
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Passive synthetic aperture processing of a towed array is basically the
coherent processing of the array data over a given length of time which, based
on the speed of tow, translates into an equivalent increase in aperture length,
and therefore a concomitant increase in spatial gain. The Kalman filter
formalism is particularly suited to this task for four reasons. First, it
coherently updates the measurements based on a comparison of their predicted
values, based on a signal model, and subsequent measurements taken at a later
time, in a recursive manner. Second, it allows in principle any signal model,
i.e., not simply plane waves. Third, it generates a minimum variance estimate
not only on the desired bearings, but any desired (observable) model
parameters. Finally, it avoids the explicit construct of a beamformer, thus
permitting the precision of the estimates to have no lower limit (such as the
spatial bin size of a beamformer) other than the minimum variance obtainable
under the given signal to noise conditions. Samples of multiple bearing
estimations based on synthetic data will be shown. Also, a general formalism
will be presented that allows direct estimation and update of certain signal
model parameters.