Maneuvering Resource Allocation for Coordinated Target Tracking in Airborne Radar Network

In this paper, a performance driven maneuvering resource allocation (MRA) scheme is developed for target tracking in airborne radar network (ARN). To exploit the degree of freedom of ARN mobility for target tracking, we formulate a MRA scheme for maximizing the target tracking performance, under the practical constraints on the airborne radar's speed and attitude variation rate, as well as threat zone and collision avoidance. We adopt the Bayesian Cramér-Rao lower bound as a metric function to gauge the target tracking performance, and build the MRA scheme as a non-convex optimization problem. Instead of using the heuristic based methods to solve the resulting non-convex optimization problem, we design an efficient three-step solution technique, which incorporates inactive constraints elimination and active constraints linearization procedure. In such a case, the resulting relaxed problem can be solved with guaranteed convergence through the proximal alternating direction method of multipliers. Simulation results demonstrate that the proposed MRA scheme can greatly increase the target tracking accuracy, and is computationally more efficient than the heuristic algorithms