In this work, we address the problem of target detection from multiple noisy observations produced by a generic sensor. A two-step approach is considered, wherein a censoring stage retains the significant measurements (i.e., those whose likelihood ratio exceeds a primary threshold) in each frame, while a multi-frame detector elaborates the pre-processed observations and takes the final decision through a generalized likelihood ratio test. A dynamic programming algorithm to form the decision statistic, which exploits the sparse nature of the censored observations, is proposed. A closed-form complexity analysis is provided, and a thorough performance assessment is undertaken to elicit the tradeoffs among censoring level, system complexity, and achievable performance.

Track-before-detect for multi-frame detection with censored observations

GROSSI, Emanuele;LOPS, Marco;VENTURINO, Luca
2014-01-01

Abstract

In this work, we address the problem of target detection from multiple noisy observations produced by a generic sensor. A two-step approach is considered, wherein a censoring stage retains the significant measurements (i.e., those whose likelihood ratio exceeds a primary threshold) in each frame, while a multi-frame detector elaborates the pre-processed observations and takes the final decision through a generalized likelihood ratio test. A dynamic programming algorithm to form the decision statistic, which exploits the sparse nature of the censored observations, is proposed. A closed-form complexity analysis is provided, and a thorough performance assessment is undertaken to elicit the tradeoffs among censoring level, system complexity, and achievable performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/28048
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