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Proceedings Paper

New concepts in time-frequency estimators with applications to ISAR data
Author(s): George A. Lampropoulos; Ekaterina Laskin; Thayananthan Thayaparan
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Paper Abstract

This paper presents a new concept for Time-Frequency estimation, which is based on algorithmic fusion. It is shown that algorithmic fusion increases considerably the detectability of signals while suppresses artifacts and noise. The paper reviews a sample of representative Time-Frequency algorithms. Their performance is studied from a qualitative and quantitative point of view. For simplicity, we have considered the Mean-Squared Error (MSE) as a measure of performance in quantitative performance evaluation studies. The algorithmic fusion is presented using a self adaptive signal and noise dependent or independent approach, while the fusion is performed using the first two terms of the Volterra Series expansion. Simplistic algorithmic fusion methods on time-frequency results (e.g. weighted averaging or weighted multiplication), are special cases of the proposed fusion technique. Experimental results are presented from simulated and real High Resolution (HR)-SAR data. Real HR-SAR data were used from the experiments performed by the Defence Research Establishment (DRDC)-Ottawa.

Paper Details

Date Published: 7 March 2003
PDF: 12 pages
Proc. SPIE 4883, SAR Image Analysis, Modeling, and Techniques V, (7 March 2003); doi: 10.1117/12.463052
Show Author Affiliations
George A. Lampropoulos, A.U.G. Signals Ltd. (Canada)
Ekaterina Laskin, A.U.G. Signals Ltd. (Canada)
Thayananthan Thayaparan, Defence Research and Development Canada (Canada)


Published in SPIE Proceedings Vol. 4883:
SAR Image Analysis, Modeling, and Techniques V
Francesco Posa, Editor(s)

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