Optimal Balancing of Multi-Function Radar Budget for Multi-Target Tracking Using Lagrangian Relaxation

The radar resource management problem in a multi-target tracking scenario for multi-function radar is considered. To solve it, an optimal balancing of the sensor budget by applying Lagrangian relaxation and the subgradient method is proposed. In a time-invariant scenario it is shown that the proposed method will lead to balanced budgets based on track parameters like maneuverability and measurement uncertainty. Moreover, since real world applications quickly lead to time-varying scenarios, it is demonstrated how the approach can be extended to such cases. Furthermore the proposed method is compared with other budget assignment strategies. This paper is the first step into exploring optimal non-myopic solutions using a POMDP framework for surveillance radar applications involving detection, tracking and classification.