Schily, HansHansSchilyCharlish, AlexanderAlexanderCharlishAdve, RavirajRavirajAdveSchmeink, AnkeAnkeSchmeink2025-06-022025-06-022024https://publica.fraunhofer.de/handle/publica/48813110.1109/RADAR58436.2024.109938832-s2.0-105005734076Modern phased array multi-function radars come with a large parameter space to optimize their operation, when servicing multiple tasks like search and tracking interleaved. Controlling multiple such radars to pursue a joint goal further increases the parameter space and makes finding the optimal set of radar parameters even more challenging. This paper proposes a new algorithm to allocate a time budget to multiple sensors that jointly optimize for search and tracking. The algorithm is compared to other multi-sensor resource allocation algorithms and an uncoordinated solution. We find that the new algorithm achieves a performance on the same level than the reference methods that jointly optimize the behaviour of the sensors, while requiring fewer model evaluations, which reduces computational requirements.enfalsecognitive radarmulti-sensoroptimizationresource managementconference paper