Nonlinear adaptive recursive least squares (NRLS) algorithm for target detection in infrared imagery

Mohammad Abu-Tahnat; Michael W. Thompson

doi:10.1117/12.241257

31 May 1996 Nonlinear adaptive recursive least squares (NRLS) algorithm for target detection in infrared imagery

Mohammad Abu-Tahnat, Michael W. Thompson

Proceedings Volume 2765, Detection and Remediation Technologies for Mines and Minelike Targets; (1996) https://doi.org/10.1117/12.241257
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

In this paper we consider the detection of small targets within an IR scene that are embedded in a dominant clutter background. A feasible approach toward addressing this issue is to invoke some form of signal processing that allows the clutter to be reduced from the scene prior to target detection. An NRLS scheme is employed which functions as whitening filter prior to matched filtering. The NRLS scheme is based on a second order truncated Volterra series expansion. The goal is to adapt to image nonstationarities and to equalize unknown system nonlinearities, prior to matched filtering. Simulation results based on both synthesized and `real world' nonstationary IR images are presented.

Citation Download Citation

Mohammad Abu-Tahnat and Michael W. Thompson "Nonlinear adaptive recursive least squares (NRLS) algorithm for target detection in infrared imagery", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); https://doi.org/10.1117/12.241257

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