Resource Allocation for Search and Track Application in Phased Array Radar Based on Pareto Bi-Objective Optimization
Junkun Yan, Wenqiang Pu, Jinhui Dai, Hongwei Liu, Zheng Bao · IEEE Transactions on Vehicular Technology · 2019
To facilitate the ability of phased array radar to manage both its search and track (SAT) tasks simultaneously within a predetermined illumination time budget, a resource allocation (RA) scheme for integrated SAT application is built in this paper. We formulate the RA scheme as a bi-objective constrained optimization framework, and use Pareto's theory to determine its multiple Pareto optimal solutions. With these Pareto solutions, one can find a suitable tradeoff between SAT tasks, and correspondingly choose an illumination scheme, for an arbitrary application demand. By exploiting the unique structure of the bi-objective optimization problem, we strictly prove that the multiple Pareto solutions with cardinality M can be obtained by parallelly solving M + 1 convex minimax problems. These problems are shown to correlate with the SAT tasks independently, and are correspondingly solved by the well-known linear programming methods and a proposed minimax solving algorithm, respectively. Finally, some numerical results are provided to illustrate the effectiveness and reveal the intrinsic mechanism of the Pareto theory based bi-objective RA strategy.