Dynamic alignment is a challenging issue for the strapdown inertial navigation system (SINS), especially in the case of no Global Position System assistance. This study proposes a novel coarse alignment algorithm for b‐frame velocity‐aided SINS. The attitude is aligned with the optimisation‐based alignment method, and the alignment process is treated in the inertial frame, so the Coriolis effect can be partly excluded. Meanwhile, the position of SINS is updated in real time by rigorous dead reckoning method. Simulation and experimental results prove that the proposed method has a good performance in the dynamic coarse alignment of SINS.

Dynamic alignment is a challenging issue for the strapdown inertial navigation system (SINS), especially in the case of no GPS assistance. A novel coarse alignment algorithm is proposed in this paper. Using external reference velocity in b-frame, the attitude is estimated by the optimization-based alignment method. The velocity algorithm of SINS is derived in the inertial coordinate frame, so the Coriolis Effect can be excluded. Meanwhile, the real-time position of the SINS is approximately located, and the deviation of the gravity vector caused by the vehicle’s displacement can be compensated, which further improves the alignment accuracy. Experimental results show that the proposed algorithm performs better than the existing algorithm and it can provide ideal initial conditions for the fine alignment.

Unmanned aerial vehicles (UAVs) have emerged as pivotal tools in emergency communication scenarios. In the aftermath of disasters, UAVs can be communication nodes to provide communication services for users in the area. In this paper, we establish a meticulously crafted virtual simulation environment and leverage advanced deep reinforcement learning algorithms to train UAVs agents. Notwithstanding, the development of reinforcement learning algorithms is beset with challenges such as sparse rewards and protracted training durations. To mitigate these issues, we devise an enhanced reward function aimed at bolstering training efficiency. Initially, we delineate a specific mountainous emergency communication scenario and integrate it with the particularized application of UAVs to undertake virtual simulations, constructing a realistic virtual environment. Furthermore, we introduce a supplementary shaped reward function tailored to alleviate the problem of sparse rewards. By refining the DQN algorithm and devising a reward structure grounded on potential functions, we observe marked improvements in the final evaluation metrics, substantiating the efficacy of our approach. The experimental outcomes underscore the prowess of our methodology in effectively curtailing training time while augmenting convergence rates. In summary, our work underscores the potential of leveraging sophisticated virtual environments and refined reinforcement learning techniques to optimize UAVs deployment in emergency communication contexts.

Unmanned aerial vehicle UAV has become an important tool for emergency communication. When disaster happens, the roads in the disaster area always are seriously damaged, and theground infrastructure communication facilities are damaged. Under emergency conditions, UAV can go into key areas as communication nodes to provide communication services for users in the area.But it's difficult for UAV to cover the entire disaster area and the efficient rescue time will be delayed. Therefore, how to make the UAV accurately perceive the regional situation and deployed in the right position is worth studying. In this paper, a virtual simulation environment is established reasonably and deep reinforcement learning algorithm is used to train UAV agents. At the same time, a series of problems such as sparse reward and long training time are still not solved in the development of reinforcement learning algorithms,and the reward function is designed to improve training efficiency. First of all, we set a specific mountain emergency communication scenario, and combined with the specific application of the UAV to carry out virtual simulation, and build a virtual environment. Furthermore, an additional shaped reward function is designed to address the sparse reward problem. Through the improvement of deep Q-learning network DQN algorithm and the reward design based on potential function, the final evaluation index is improved, and the effectiveness of the algorithm is verified. The experimental results demonstrate our work can effectively shorten the training time and increase convergencerate.