Low Earth Orbit (LEO) satellite communication is gradually becoming the main carrier for satellite communication by virtue of its advantages, such as high landing power, narrow beam, large transmission bandwidth, and small time delay. In the military field, interference with LEO satellites has become a core element in combat, but the existing interference and confrontation methods cannot meet the needs of LEO satellite interference. Aiming at the above problems, this paper proposes an LEO satellite navigation signal multi-dimensional interference optimisation method based on hybrid game theory. Firstly, the method achieves a dynamic classification of jammers within the airspace. Then, an interference effectiveness evaluation function is established, which reflects the time, frequency, and power domain losses, as well as the strategy gains. With the help of hybrid game theory, the optimal resource allocation under Nash equilibrium is achieved, and the distributed interference optimisation problem is effectively solved. The experiment uses a large microwave darkroom as an interference verification scenario. The results indicate that the interference bit error rate (BER) of the algorithm proposed in this paper is on the order of ${10}^{-2}$, under the premise of guaranteeing the full coverage of the area to be interfered. The value of the multidimensional interference utility function, including the power, time, and frequency domains, is improved by at least 0.4993 times compared to other algorithms.

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods.

Unmanned Aerial Vehicle (UAV) clusters have broad applications in agricultural detection, traffic control, and disaster rescue, where navigation and positioning serve as the core technology. However, satellite navigation fails to meet the requirements of region-wide navigation due to the urban canyon effect. Although the China Satellite Network (CSN) boasts advantages such as high landing power and low latency, it can only achieve single-link communication. Consequently, exploring how to realize cooperative positioning via UAV clusters has become an urgent research need. In this paper, an Unmanned Aerial Vehicle Cluster Communication–Navigation Integrated Cooperative Positioning (UCNCP) algorithm is proposed. This algorithm combines the communication and navigation characteristics of the CSN, establishes a single pseudorange measurement model and cluster geometric topology, and constructs an architecture for cooperative positioning based on UAV cluster pseudorange measurements and inter-UAV ranging data, thereby achieving reliable navigation and positioning of UAV clusters. Comparative experiments between the proposed method and other low-orbit satellite positioning methods demonstrate that the UCNCP algorithm exhibits higher positioning stability. When abrupt changes occur in navigation information, it can effectively mitigate the impact of abrupt change errors on positioning accuracy, improving the positioning stability of UAV clusters by more than 30%.