This paper introduces a novel cross-modal neural network model that aims to convert long-term temporal behavior data into natural language to achieve UAV intent recognition. Our generative intent recognition model effectively utilizes the inherent redundancy present in long temporal behavior data by incorporating a sequence compression module, which enables the cross-modal generation and alignment of intents while preserving the integrity of the standard Transformer architecture. Importantly, we observe that this approach mitigates the negative impact of imbalanced database distribution by mapping intent categories onto the modality of natural language. Furthermore, we propose three comprehensive pre-training tasks specifically designed for time series data, thoroughly examining their interconnections and analyzing the impact of a hybrid pre-training framework on the accuracy of intent recognition. Our experimental results demonstrate the superiority of our proposed generative UAV intent recognition model, along with the hybrid pre-training initialization method, compared to conventional classification models. Simultaneously, the intent recognition method exhibits heightened temporal sensitivity and robust resilience, enabling it to deal with complex UAV confrontation and interference environment.

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

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.

The coordination of heterogeneous Unmanned Aerial Vehicles (UAVs) for complex, multi-stage tasks presents a significant challenge in robotics and autonomous systems. Traditional linear models often fail to capture the emergent synergistic effects and dynamic nature of multi-agent collaboration. To address these limitations, this paper proposes a novel hierarchical framework based on a Mission Chain (MC) concept. We systematically define and model key elements of multi-agent collaboration, including Mission Chains (MCs), Execution Paths (EPs), Task Networks (TNs), and Solution Spaces (SSs), creating an integrated theoretical structure. Based on this framework, we formulate the problem as a Sensor–Effector–Target Assignment challenge and propose a Marginal Return-Based Heuristic Algorithm (MRBHA) for efficient dynamic task allocation. Simulations demonstrate that our proposed MRBHA achieves a substantially higher total expected mission value—outperforming standard greedy and random assignment strategies by 14% and 77%, respectively. This validates the framework’s ability to effectively capitalize on synergistic opportunities within the UAV network. The proposed system provides a robust and scalable solution for managing complex missions in dynamic environments, with potential applications in search-and-rescue, environmental monitoring, and intelligent logistics.

Effective situation awareness relies on the robust processing of high-dimensional data streams generated by onboard sensors. However, the application of deep generative models to extract features from complex UAV sensor data (e.g., GPS, IMU, and radar feeds) faces two fundamental challenges: critical training instability and the difficulty of representing multi-modal distributions inherent in dynamic flight maneuvers. To address this, this paper proposes a novel unsupervised sensor data processing framework to overcome these issues. Our core innovation is a deep generative model, VAE-WRBM-MDN, specifically engineered for stable feature extraction from non-linear time-series sensor data. We demonstrate that while standard Variational Autoencoders (VAEs) often struggle to converge on this task, our introduction of Weighted-uncertainty Restricted Boltzmann Machines (WRBM) for layer-wise pre-training ensures stable learning. Furthermore, the integration of a Mixture Density Network (MDN) enables the decoder to accurately reconstruct the complex, multi-modal conditional distributions of sensor readings. Comparative experiments validate our approach, achieving 95.69% classification accuracy in identifying situational patterns. The results confirm that our framework provides robust enabling technology for real-time intelligent sensing and raw data interpretation in autonomous systems.

Aerial situational awareness (SA) faces significant challenges due to inherent complexity involving large-scale dynamic entities and intricate spatio-temporal relationships. While deep learning advances SA for specific data modalities (static or time-series), existing approaches often lack the holistic, vision-centric perspective essential for human decision-making. To bridge this gap, we propose a unified GNN-CV framework for operational-level SA. This framework leverages mature computer vision (CV) architectures to intelligently process radar-map-like representations, addressing diverse SA tasks within a unified paradigm. Key innovations include methods for sparse entity attribute transformation graph neural networks (SET-GNNs), large-scale radar map reconstruction, integrated feature extraction, specialized two-stage pre-training, and adaptable downstream task networks. We rigorously evaluate the framework on critical operational-level tasks: aerial swarm partitioning and configuration recognition. The framework achieves an impressive end-to-end recognition accuracy exceeding 90.1%. Notably, in specialized tactical scenarios featuring small, large, and irregular flight intervals within formations, configuration recognition accuracy surpasses 85.0%. Even in the presence of significant position and heading disturbances, accuracy remains above 80.4%, with millisecond response cycles. Experimental results highlight the benefits of leveraging mature CV techniques such as image classification, object detection, and image generation, which enhance the efficacy, resilience, and coherence of intelligent situational awareness.