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The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model's training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field. [ABSTRACT FROM AUTHOR]

Multi-stage launch vehicles are currently the primary tool for humans to reach extraterrestrial space. The technology of recovering and reusing rockets can effectively shorten rocket launch cycles and reduce space launch costs. With the development of deep representation learning, reinforcement learning (RL) has become a robust learning framework capable of learning complex policies in high-dimensional environments. In this paper, a deep reinforcement learning-based reusable rocket landing control method is proposed. The mathematical process of reusable rocket landing is modelled by considering the aerodynamic drag, thrust, gravitational force, and Earth's rotation during the landing process. A reward function is designed according to the rewards and penalties derived from mission accomplishment, terminal constraints, and landing performance. Based on this, the Softmax double deep deterministic policy gradient (SD3) deep RL method is applied to build a robust reusable rocket landing control method. In the constructed simulation environment, the proposed method can achieve convergent and robust control results, proving the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

While deep learning has shown its promises in solving spacecraft optimal control problems, it still requires a large amount of training samples and is lack of the interpretability. In this article, we introduce the nonparametric flexibility of kernel approaches into the deep learning architecture, and develop a deep kernel learning model to provide an interpretable control prediction in various aerospace missions. In our framework, the core is a Gaussian process function with RBF kernel that can transform the latent features into infinite-dimensional latent space to obtain interpretable similarities. It hence intuitively brings the positive or negative correlations between the predicted result and all training samples. The parameters of both the deep architecture and the kernel are jointly trained in an end-to-end manner. Simulation results in two aerospace missions show that the proposed model outperforms the stand-alone deep neural network model and the Gaussian process model in control prediction accuracy and interpretability under insufficient training samples. [ABSTRACT FROM AUTHOR]

This paper presents wideband split-ring antenna arrays based on substrate integrated waveguide (SIW) for Ka-band (26.5–40 GHz) applications. The antenna array is fed by a 2.92 mm coaxial connector (K-connector) and the power is equally distributed to each split-ring resonator. The designed coplanar waveguide (CPW), SIW, CPW-to-SIW transition, coaxial-to-CPW transition, and two-stage SIW power divider are described in detail. By using a thin Rogers 6002 substrate with silver epoxy-filled vias, a transition prototype is designed, fabricated, and tested in a back-to-back configuration. A wideband split-ring resonator is developed as a single element and four possible arrangements of antenna arrays are introduced. By combining the designed components and routing paths, two full layouts of the antenna arrays with four split-ring resonators are addressed. As a demonstrator, a 2×2 antenna array prototype in a compact format is designed, fabricated, and tested. The fabricated antenna array achieves a measured directivity of 15.0 dBi with a fractional bandwidth of 23.0% centered at 30.5 GHz. [ABSTRACT FROM AUTHOR]