Collaborative Search Approach for Autonomous Underwater Vehicle Swarm-Based Distributed Radar in Communication Denied Environments

The autonomous underwater vehicle (AUV) are experiencing widely deployment for the advantages of low cost and flexibility. However, the open light-of-sight communication links between clusters to clusters (C2C) in flying ad-hoc networks (FANETs) are vulnerable to malicious jamming in communication denied environments (CDEs), leading to the C2C communication with low-signal-to-interference-plus-noise ratio (SINR). Aiming at the collaborative search inefficiency resulting from unreliable C2C communication caused by low SINR in existing researches, we propose a distributed collaborative search planning method based on anti-jamming strategy by virtual multiple input-multiple output (VMIMO) in FANETs. First, we formulate the anti-jamming C2C collaborative communication model based on VMIMO to combat the malicious jamming utilizing the advantage of large-scale AUVs under limited resource. Then, we model the overall search objective function, considering search, connectivity and collision avoidance between AUV clusters. And we establish a distributed collaborative search optimization problem model based on distributed model predictive control (DMPC) framework, so that all AUV clusters can optimize the overall search objective by interacting with neighbor clusters. Finally, we propose a three-level collaborative optimization problem model with joint considering search, anti-jamming communication and network connectivity. To calculate the optimal solution, an alternating optimization method based on distributed stochastic algorithm is proposed to solve the three-level collaborative optimization problem model. Moreover, we conduct extensive simulations and the results show that the proposed method is efficient in terms of the anti-jamming ability and search efficiency compared with other state-of-the-art algorithms without cooperative strategy.