In this paper, the mathematical functional model of the optimized baseband symbol signals is established in accordance with the criterion for minimized out-of-band energy radiation. The functional is limited with the restrictions on conditions of boundary, single baseband symbol signal energy and constant envelope. With the introduction of Legendre series, the baseband symbol signals with optimized efficient spectrum are obtained by determining the minimum of the functional. The properties of spectrum and PAPR (peak-to-average power ratio) of the obtained signals are analysed and compared with MSK signals. A scheme for quadrature modulation and coherent demodulation is presented, and the simulation of quadrature modulation and coherent demodulation is achieved by using MATLAB, the simulation results demonstrate that the method is available.

Deep neural networks have been widely used in detection tasks based on optical remote sensing images. However, in recent studies, deep neural networks have been shown to be vulnerable to adversarial examples. Adversarial examples are threatening in both the digital and physical domains. Specifically, they make it possible for adversarial examples to attack aerial remote sensing detection. To defend against adversarial attacks on aerial remote sensing detection, we propose a cascaded adversarial defense framework, which locates the adversarial patch according to its high frequency and saliency information in the gradient domain and removes it directly. The original image semantic and texture information is then restored by the image inpainting method. When combined with the random erasing algorithm, the robustness of detection is further improved. Our method is the first attempt to defend against adversarial examples in remote sensing detection. The experimental results show that our method is very effective in defending against real-world adversarial attacks. In particular, when using the YOLOv3 and YOLOv4 algorithms for robust detection of single-class targets, the AP60 of YOLOv3 and YOLOv4 only drop by 2.11% and 2.17%, respectively, under the adversarial example.

Three-axis air-bearing test bed is important semiphysical simulation equipment for spacecraft, which can simulate spacecraft attitude control, rendezvous, and docking with high confidence. When the three-axis air-bearing table is maneuvering at a large angle, if it is only controlled by the flywheel, it will cause the problems of slow maneuvering speed and high energy consumption, and when the external interference torque becomes large, the control accuracy will decline. A combined actuator including flywheel, air-conditioner thruster, and automatic balancing device is designed, and a hierarchical saturation PD control algorithm is proposed to improve the control accuracy and anti-interference ability of the three-axis air-bearing test bed. Finally, the mathematical simulation of the proposed control algorithm is carried out, and the physical verification is carried out on the three-axis air-bearing test bed. The results show that the control algorithm has higher control accuracy than the traditional control algorithm, and the control accuracy is better than and basically meets the attitude control requirements of the ground simulation in-orbit satellite.

Deep neural networks (DNNs) have been shown to be susceptible to slightly perturbed adversarial examples. Even adversarial examples that are imperceptible to the human eye can easily lead to misclassification of DNNs. The most effective defense against adversarial examples is adversarial training (AT). Through AT, the robustness of the model against adversarial examples can be greatly improved. However, AT causes a decrease in the accuracy of the model’s classification of natural examples and serious overfitting problems. To improve the generalization ability of the model, we propose a method for enhancing model robustness through augmented data importance perception (ADIP). By extracting offensive adversarial examples for data augmentation and considering the attack strength of new adversarial examples to design an importance measure term to improve the loss function, the results of experiments show that the model robustness enhancement method based on ADIP improves the robustness of the model and alleviates the overfitting problem of the model caused by AT. In addition, our algorithm trains a robust model with a small computational cost.