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

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

Edmund J. Sullivan

Naval Undersea Warfare Ctr., Newport, RI 02841

James V. Candy

Lawrence Livermore Natl. Lab., Univ. of California, Livermore, CA 94550

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.