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.