Dimension-reduced adaptive pulse compression with model error compensation

Abstract

In active acoustic imaging, range sidelobes from competing clutter and interferers in acoustically complex environments may mask signals of interest. In order to unmask these signals, increase range resolution, and decorrelate the time-series we introduce dimension-reduced adaptive pulse compression (APC) with model-error compensation that operates after a conventional matched filter. Structured covariance estimates minimize the required sample support while dimension reduction occurs naturally and a priori after the matched filter. We compensate for predictable signal distortions such as Doppler through both covariance matrix tapers and truncation of the covariance matrix. Model-based APC is computationally intensive, scaling with the third or fourth power with replica length, so algorithmic modifications are introduced to reduce computation by orders of magnitude. We evaluate the algorithm's robustness and computational feasibility on simulated and archived data. [Portions of this material are based upon work supported by the Naval Sea Systems Command.]