关键词: 时间图卷积网络, 协同定位, 通道剪枝, 图重排算法, 深度确定性策略梯度

Abstract: With the rapid development of UAV technology, it has become a difficult research problem to locate UAV in the absence of positioning signal. In recent years, the emergence and development of graph neural network provides a new way to solve this problem. However, the deployment of image neural networks in resource-constrained UAVs faces challenges such as limited storage resources and difficult real-time performance. Under this background, an airborne depth calculation and compilation optimization method for UAV collaborative positioning is proposed in this paper. Specifically, we use a lightweight time graph convolutional neural network model, which consists of a graph convolutional network and a gated recursive unit. The spatial dependence of UAV swarm and the time dependence of UAV position change are considered at the same time, and the position of UAV swarm is accurately predicted. Aiming at the redundancy of the model in the time-graph convolutional network, a global adaptive pruning algorithm based on inverse Cuthill-McKee graph rearrangement and double-depth deterministic strategy gradient is proposed. While ensuring the accurate prediction of UAV group coordinates, it can not only improve the spatial locality of data in main memory, accelerate the computing speed of the model, but also carry out adaptive unstructured pruning on the model and reduce the storage complexity of the model. The experimental results show that compared with the existing time graph convolutional neural network model, the compiled and optimized lightweight time graph convolutional neural network model retains 78.8% accuracy, while the calculation time of the model is reduced by 37.9%, and the average pruning rate of the model reaches 90.3%.

Key words: temporal graph convolutional network, cooperative localization, channel pruning, graph rearrangement algorithm, deep deterministic policy gradient