The radar resource management problem in a multitarget tracking scenario is considered. The problem is solved using a dynamic budget balancing algorithm. It models the different sensor tasks as partially observable Markov decision processes and solves them by applying a combination of Lagrangian relaxation and policy rollout. The algorithm has a generic architecture and can be applied to different radar or sensor systems and cost functions.This is shown through simulations of two-dimensional tracking scenarios. Moreover, it is demonstrated how the algorithm allocates the sensor time budgets dynamically to a changing environment in a nonmyopic fashion. Its performance is compared with different resource allocation techniques and its computational load is investigated with respect to several input parameters.
|Number of pages||47|
|Journal||ISIF Journal of Advances in Information Fusion|
|Publication status||Published - 2021|
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- Radar Resource Management
- Partially Observable Markov Decision Process
- Lagrangian Relaxation