Online Frequency-Agile Strategy for Target Detection in Distributed Radar Networks

The development of low-observable target technology poses significant challenges to weak target detection. This paper formulates the distributed target detection problem for radar networks using the multi-player multi-armed bandit (MMAB) framework, a probabilistic decision-making model, and proposes an online cooperative cognitive frequency-hopping strategy. This approach makes optimal use of both the target’s frequency-division and angular-spread characteristics and optimizes the number of radars involved in detection. It employs biased estimation of radar transceiver band detection results through the discounted upperconfidence bound (DUCB) strategy, which helps preserve frequency band orthogonality, and facilitates radar band selection without communication through a collaborative procedure,MCtopM. Simulation results demonstrate that this strategy significantly reduces mutual interference within the radar network, thereby improving spectrum utilization and enhancing the network’s ability to detect targets more efficiently and accurately.