Distributed Optimization of Resource Allocation for Search and Track Assignment with Multifunction radars

Title of dissertation: DISTRIBUTED OPTIMIZATION OF RESOURCE ALLOCATION FOR SEARCH AND TRACK ASSIGNMENT WITH MULTIFUNCTION RADARS Tracie A. Severson, Doctor of Philosophy, 2013 Dissertation directed by: Dr. Derek A. Paley Department of Aerospace Engineering and the Institute for Systems Research The long-term goal of this research is to contribute to the design of a conceptual architecture and framework for the distributed coordination of multifunction radar systems. The specific research objective of this dissertation is to apply results from graph theory, probabilistic optimization, and consensus control to the problem of distributed optimization of resource allocation for multifunction radars coordinating on their search and track assignments. For multiple radars communicating on a radar network, cooperation and agreement on a network resource management strategy increases the group’s collective search and track capability as compared to non-cooperative radars. Existing resource management approaches for a single multifunction radar optimize the radar’s configuration by modifying the radar waveform and beam-pattern. Also, multi-radar approaches implement a top-down, centralized sensor management framework that relies on fused sensor data, which may be impractical due to bandwidth constraints. This dissertation presents a distributed radar resource optimization approach for a network of multifunction radars. Linear and nonlinear models estimate the resource allocation for multifunction radar search and track functions. Interactions between radars occur over time-invariant balanced graphs that may be directed or undirected. The collective search area and target-assignment solution for coordinated radars is optimized by balancing resource usage across the radar network and minimizing total resource usage. Agreement on the global optimal target-assignment solution is ensured using a distributed binary consensus algorithm. Monte Carlo simulations validate the coordinated approach over uncoordinated alternatives.