7 March 2003 New concepts in time-frequency estimators with applications to ISAR data
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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.
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George A. Lampropoulos, Ekaterina Laskin, Thayananthan Thayaparan, "New concepts in time-frequency estimators with applications to ISAR data", Proc. SPIE 4883, SAR Image Analysis, Modeling, and Techniques V, (7 March 2003); doi: 10.1117/12.463052; https://doi.org/10.1117/12.463052
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