On distributed signal detection with multiple local free parameters

In this work, we propose an efficient approach to the optimization of distr ibuted 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 t he locally optimum detection (LOD) criteria. It avoids the exhaustive searc h for the optimal discrete parameters and greatly reduces the computational load required for global system optimization. The effectiveness of the pro posed approach is demonstrated by means of a numerical example, where order ed statistic (OS) constant false-alarm rate (CFAR) decentralized radar dete ction of Swerling I targets in Gaussian noise is considered.