In the field of satellite imagery, remote sensing image captioning (RSIC) is a hot topic with the challenge of overfitting and difficulty of image and text alignment. To address these issues, this paper proposes a vision-language aligning paradigm for RSIC to jointly represent vision and language. First, a new RSIC dataset DIOR-Captions is built for augmenting object detection in optical remote (DIOR) sensing images dataset with manually annotated Chinese and English contents. Second, a Vision-Language aligning model with Cross-modal Attention (VLCA) is presented to generate accurate and abundant bilingual descriptions for remote sensing images. Third, a cross-modal learning network is introduced to address the problem of visual-lingual alignment. Notably, VLCA is also applied to end-to-end Chinese captions generation by using the pre-training language model of Chinese. The experiments are carried out with various baselines to validate VLCA on the proposed dataset. The results demonstrate that the proposed algorithm is more descriptive and informative than existing algorithms in producing captions.

In this paper, more efficient allocation of forces is analyzed in the future air confrontation among unmanned aerial vehicle swarms. A novel method is proposed for swarm confrontation based on the Lanchester law and Nash equilibrium. Due to the huge number of unmanned aerial vehicles, it is not beneficial to deploy UAV forces in swarm confrontation. Moreover, unmanned aerial vehicles do not have high maneuverability in collaboration. Therefore, we propose to divide the swarms of unmanned aerial vehicles into groups, so that swarms of both sides can fight in different battlefields, which could be considered as a Colonel Blotto Game. Inspired by the double oracle algorithm, a Nash equilibrium solving method is proposed to searched for the best force allocation of the swarm confrontation. In addition, this paper proposes the concept of the boundary contact rate and carries out quantitative numerical analysis with the Lanchester law. Experiments reveal the relationship between the boundary contact rate and the optimal strategy of swarm confrontation, which could guide the force allocation in future swarm confrontation. Furthermore, the effectiveness of the division method and the double oracle-based equilibrium solving algorithm proposed in this paper is verified.

This study addressed a problem of rapid velocity consensus within a swarm of unmanned aerial vehicles. Our analytical framework was based on tools using matrix theory and algebraic graph theory. We established connections between algebraic connectivity and the speed of converging on a velocity. The relationship between algebraic connectivity and communication cost was established. To deal with the trade-off among algebraic connectivity, convergence speed and communication cost, we propose a distributed small world network construction method. The small world network characteristics expedite the convergence speed toward consensus in the unmanned aerial vehicle swarm. Eventually, our method greatly sped up the consensus velocities in the unmanned aerial vehicle swarms at a lower communication cost than other methods required.

In addressing the challenge of tracking moving targets at sea, our focus has been directed towards the development of a reconstruction methodology founded upon satellite orbital manoeuvres. This endeavour has led us to devise a predictive model for manoeuvres within a geographic coordinate system, alongside the creation of a three-phase orbital manoeuvre model. A Non-dominant Sorting Adaptive Memetic (NSAM) algorithm is proposed in this paper, which is a two-layer multi-objective optimization algorithm that retains the advantages of evolutionary algorithms based on the population’s evolution and has an excellent local optimization ability of local search algorithms. The proposed algorithm can be used to solve multi-objective optimization problems. By comparing the target observation results before and after the satellite reconstruction simulation, it can be concluded that the orbital manoeuvring can effectively improve the observation probability and observation duration of the target at a certain speed. The orbital manoeuvre model created in this paper provides a certain methodical support for the tracking problem of moving targets at sea.

The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in the adversarial environment. It is computationally expensive to cover a sufficient number of rewarded states that are far away from the root in the flat non-hierarchical MCTS. Therefore, the flat non-hierarchical MCTS is inefficient for dealing with planning problems with a long planning horizon, huge state space, and branching factors. In this work, we propose a novel hierarchical MCTS-based online planning method named the HMCTS-OP to tackle this issue. The HMCTS-OP integrates the MAXQ-based task hierarchies and the hierarchical MCTS algorithms into the online planning framework. Specifically, the MAXQ-based task hierarchies reduce the search space and guide the search process. Therefore, the computational complexity is significantly reduced. Moreover, the reduction in the computational complexity enables the MCTS to perform a deeper search to find better action in a limited time. We evaluate the performance of the HMCTS-OP in the domain of online planning in the asymmetric adversarial environment. The experiment results show that the HMCTS-OP outperforms other online planning methods in this domain.