On distributed signal detection with multiple local free parameters

Fulvio Gini; Fabrizio Lombardini; Pramod K. Varshney

doi:10.1109/7.805464

On distributed signal detection with multiple local free parameters

Fulvio Gini, Fabrizio Lombardini, Pramod K. Varshney

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

In this work, we propose an efficient approach to the optimization of distributed multiradar systems with parallel topology, employing decision fusion from local detectors with discrete and continuous free-design parameters. This approach, termed hierarchical optimization approach, can be applied to a variety of optimization criteria including the Neyman-Pearson (NP) and the locally optimum detection (LOD) criteria. It avoids the exhaustive search for the optimal discrete parameters and greatly reduces the computational load required for global system optimization. The effectiveness of the proposed approach is demonstrated by means of a numerical example, where ordered statistic (OS) constant false-alarm rate (CFAR) decentralized radar detection of Swerling I targets in Gaussian noise is considered.

Original language	English (US)
Pages (from-to)	1457-1466
Number of pages	10
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	35
Issue number	4
DOIs	https://doi.org/10.1109/7.805464
State	Published - 1999

ASJC Scopus subject areas

Aerospace Engineering
Electrical and Electronic Engineering

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10.1109/7.805464

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AB - In this work, we propose an efficient approach to the optimization of distributed multiradar systems with parallel topology, employing decision fusion from local detectors with discrete and continuous free-design parameters. This approach, termed hierarchical optimization approach, can be applied to a variety of optimization criteria including the Neyman-Pearson (NP) and the locally optimum detection (LOD) criteria. It avoids the exhaustive search for the optimal discrete parameters and greatly reduces the computational load required for global system optimization. The effectiveness of the proposed approach is demonstrated by means of a numerical example, where ordered statistic (OS) constant false-alarm rate (CFAR) decentralized radar detection of Swerling I targets in Gaussian noise is considered.

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On distributed signal detection with multiple local free parameters

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