低空通感网络中运动状态传感器辅助的同步定位与散射环境建图OA

With the widespread application of the low-altitude economy and unmanned aerial vehicles(UAVs),low-altitude integrated sensing and communication(ISAC)networks have an increasingly urgent demand for high-precision 3D localization and scattering environment mapping.To address the problems of complex multi-slot joint inference,difficulty in maintaining environmental representation consistency,and insufficient inter-slot prior propagation in existing simultaneous localization and mapping(SLAM)methods under complex low-altitude channels and dynamic scatterer birth-death,this paper proposes a motion state sensor-assisted SLAM method.By exploiting velocity,acceleration,and altitude priors provided by commonly equipped motion state sensors,the multi-slot Bayesian inference problem is decomposed into per-slot subproblems with temporal message passing,and solved by an expectation-maximization based subspace-constrained successive linear approximation variational Bayesian inference algorithm to reduce computational complexity and improve localization and mapping robustness in dynamic scenarios.Simulation results show that the proposed method outperforms classical methods and achieves more accurate localization of both UAV terminals and scatterers.