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.
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