Dual-Driven Pattern-Coupled Sparse Bayesian Learning Unfolding Network for Decentralized Noncoherent DoA Estimation in UAV Swarm

The collaboration among multiple unmanned aerial vehicles (UAVs) can overcome the limitation of spatial sensing capabilities of individual UAVs has attracted extensive attention in the field of direction-of-arrival (DoA) estimation. Considering the constrained hardware resources in UAV swarm, maintaining coherence among all elements is extremely challenging. In this paper, we consider a collaborative UAV swarm with partly calibrated subarrays and propose a dual-driven joint-sparse pattern-coupled sparse Bayesian learning unfolding network (DD-JPC-SBLNet) for non-coherent DoA estimation. Specifically, we first propose a novel joint-sparse pattern-coupled sparse Bayesian learning (JPC-SBL) algorithm, which introduces a pattern-coupled model and a distributed noise variance estimation module, to improve the non-coherent DoA estimation accuracy. Then, the JPC-SBL algorithm is unfolded into cascaded customized neural networks, each of which learns the optimal coupling parameter configuration based on the pattern-coupled model and learns the current optimal signal hyperparameter update rule with a data-driven customized neural network. By effectively combining the advantages of model-driven and data-driven, the proposed dual-driven unfolding network exhibits superior convergence performance and speed. Simulation results demonstrate that the proposed method not only outperforms existing methods in terms of estimation accuracy and angular resolution, but also reduces computational complexity by more than 50% compared with other SBL-based algorithms.