Unsupervised Contrastive Learning for Automatic Grouping of Aerial Swarms

The self-organization capabilities of massive aerial swarms pose challenges to conventional methods of grouping aerial targets. These traditional approaches struggle with issues such as stability, reliability, and the recognition of deep intentions. To overcome these challenges, we propose an architecture called Unsupervised Contrastive Learning for Aerial Targets Grouping (UCL-ATG). Our approach utilizes random time periods within the scenario as training batches for contrastive learning. The UCL-ATG model consists of four key modules: feature extraction, time series generation, fine-tuning, and clustering. The feature extraction and time series generation modules form the network architecture of Contrastive Predictive Coding (CPC). Positive and negative samples are obtained using different sampling methods. To effectively collaborate with the clustering module, we design a contrastive predictive loss function specifically tailored for clustering. This enables the extraction of low-dimensional representations that capture the high-dimensional temporal characteristics of aircraft. Furthermore, we introduce a real-time fine-tuner to enhance the model's transferability to specific tasks. The inclusion of the real-time fine-tuner greatly alleviates problems associated with evolving confrontation styles and unevenly distributed datasets, as confirmed by the performance on the validation set. Extensive comparative experimental results demonstrate our model's superior training outcomes and its ability to extract improved clustering features. In wargame applications, our model no longer relies solely on static position information of the aircraft. It exhibits outstanding capabilities in historical information memory, information synthesis, and even demonstrates aptitude in identifying friend or foe and making tactical inferences.