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Proceedings Paper

Adaptive model-based 3-D target detection--Part I: modeling and algorithms
Author(s): Mac L. Hartless; Hong Wang
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Paper Abstract

This paper investigates methods for adaptive detection of a 3D (space-time) LR/EO target in clutter of unknown and possibly non-stationary statistics. Non-stationary data conditions necessitate estimation of the environmental parameters over local regions. A low degree-of-freedom model for the space-time clutter characteristics is assumed. This allows the model based matched filter to adapt to the changing clutter characteristics better than the usual technique of matched filter construction, e.g. direct sample matrix inversion (SMI). The models used for characterizing the space-time clutter characteristics include a 3D autoregressive model, a non-causal minimum variance representation model, and a space-time separable clutter model The matched filter algorithms are derived for these models using the estimated model parameters. In addition, the signal-to-noise ratios (SNR) that are achievable in stationary clutter conditions by the model-based filters are compared to that obtained by the optimal linear filter for a variety of clutter and target characteristics. An analysis of losses due to target/clutter mis-modeling and other effects are also presented.

Paper Details

Date Published: 25 August 1992
PDF: 12 pages
Proc. SPIE 1698, Signal and Data Processing of Small Targets 1992, (25 August 1992); doi: 10.1117/12.139404
Show Author Affiliations
Mac L. Hartless, GE Aerospace (United States)
Hong Wang, Syracuse Univ. (United States)


Published in SPIE Proceedings Vol. 1698:
Signal and Data Processing of Small Targets 1992
Oliver E. Drummond, Editor(s)

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