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Linear prediction for single snapshot multiple target doppler estimation under possibly moving radar clutter

Öztan, Baha Baran
We have devised a processor for pulsed Doppler radars for multi-target detection in same folded range under land and moving clutter. To this end, we have investigated the estimation of parameters, i.e., frequencies, amplitudes, and phases, of complex exponentials that model target echoes under radar clutter characterized by antenna scanning modulation with observation limited to single snapshot, i.e., one burst. The Maximum Likelihood method of estimation is presented together with the bounds on estimates, i.e., Cramér-Rao bounds. We have analyzed linear prediction, together with its efficient implementation invented by Tufts & Kumaresan, and compared its performance to other high resolution frequency estimation algorithms all modified to run under clutter. The essential part of the work is that line spectra estimation techniques model the clutter process also as a complex exponential. In addition, linear prediction combined with linear timeinvariant maximum Signal to Interference Ratio (SIR) processor is analyzed. A technique to determine the model order, which is required by the frequency estimation algorithms, is presented that does not distinguish between targets and clutter. Clutter region concept is introduced to identify targets from clutter. The possibility to use these algorithms for target classification is briefly explained after providing a literature survey on helicopter echoes.