Abstract: As a key technology of future intelligent transportation systems(ITS), unmanned autonomous vehicles(UAVs) have become a research hotspot in recent years. As an emerging intelligent transportation tool, unmanned vehicles rely on the precise location observations provided by navigation sensors. Once compromised, navigation sensors may generate abnormal observations deviating away from the ground truth, which as a result will cause severe consequences or even fatal accidents. To enhance the security of UAVs, a new deep learning based anomaly diagnosis method is proposed in this paper for the detection and identification of sensor anomalies in UAVs. The proposed method improves the topological structure of the traditional 1D Convolutional neural network (1D-CNN) and designs a 1DGAP-CNN algorithm framework to achieve a real-time rapid diagnosis. First, the original pose measurements from multiple sensors are directly input into the proposed algorithm for data fusion and preprocessing. Secondly, the proposed algorithm automatically performs feature extraction, dimension transformation, parameter reduction, and anomaly identification. Finally, the diagnosis results are automatically generated. Evaluation results show that the proposed method has higher diagnostic accuracy and faster detection speed than the state-of-the-art intelligent diagnostic algorithms.