The log-sum function as a penalty has always been drawing widespread attention in the field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz optimization problem that is difficult to tackle. To overcome the problem, an iterative threshold algorithm for the sparse optimization problems with log-sum function is proposed in this paper. For brevity, the sparse optimization problems with log-sum function are named log-sum regularization. Firstly, by introducing an intermediate function to construct another new function, a property theorem about solution for log-sum regularization is established. Secondly, based on the above theorem, the optimal setting rules of the compromising parameters are elaborated, and an iterative log-sum threshold algorithm is proposed. Thirdly, under the situation that the compromising parameters of log-sum regularization are relatively small, it can be proven that the proposed algorithm converges to a local minimizer of log-sum regularization. Finally, a series of simulations are implemented to examine performance of the proposed algorithm, and the results exhibit that the proposed algorithm outperforms the state-of-the-art algorithms.

Inverse synthetic aperture radar (ISAR) can only provide two-dimensional (2-D) images to represent the target's scattering projection on the corresponding imaging planes. However, as the echo of the target from different observation angles can be achieved simultaneously, the radar network can provide 3-D scattering information about the target. In this article, a novel 3-D scattering image reconstruction method is proposed based on the radar network and compressed sensing (CS). First, the general signal model and the reconstruction conditions of the radar network 3-D reconstruction are given. Then, the 3-D scattering distribution reconstruction model is built in a CS framework, which can reconstruct the positions and coefficients of scattering centers simultaneously. Moreover, the sparse structure with three layers in radar network 3-D reconstruction is defined and a fast 3-D reconstruction algorithm is proposed. To the end, the numerical simulations under noise scenarios and the principle prototype experiments on real data are shown to demonstrate the validity of the proposed method.

In this paper, contrapose the impendency for multi-target high-resolution imaging with the limited resources, a radar network time scheduling is proposed based on game theory and reinforcement learning for inverse synthetic aperture radar (ISAR) imaging task regarding the targets in different radar beams. According to the demand for using the least amount of time to achieve the expected imaging resolution, the radar observation time scheduling problem is formulated. The game behaviour in the optimization problem is analyzed, and a time scheduling game is constructed to acquire the time scheduling strategy. For the purpose of finding out the optimal strategy profile, an equilibrium-based multiagent reinforcement learning (MARL) for the time scheduling game is proposed. Simulation results demonstrate that the time scheduling game belongs to exact potential game and can converge to the optimal strategy profile of the radar observation time scheduling problem by the proposed equilibrium-based MARL. Besides, the learning ability of the equilibrium-based MARL is proved.

The measurement-device-independent (MDI) quantum key distribution (QKD) is considered to be an alternative to overcome the currently trusted satellite paradigm. However, the feasibility of the space-based MDI-QKD remains unclear in terms of the factors: the high-loss uplink between a ground station and a satellite, the limited duration when two ground stations are simultaneously visible, as well as the rigorous requirements for the two-photon interference when performing the Bell-state measurement. In this paper, we present a feasibility assessment of space-based MDI-QKD based on the Micius satellite. Integrated with the orbital dynamics model and atmosphere channel model, a framework is presented to explore the whole parameters space including orbit height, elevation angle, apertures of transceiver and atmospheric turbulence intensity to give the considerations for improving key rates and subsequently provide a relevant parameter tradeoff for the implementation of space-based MDI-QKD. We further investigate the heart of MDI-QKD, the two-photon interference considerations such as the frequency calibration and time synchronization technology against Doppler shift, and the way of performing the intensity optimization method in the dynamic and asymmetric channels. Our work can be used as a pathfinder to support decisions involving as the selection of the future quantum communication satellite missions.

Achieving low-power and polarization-insensitive communication is a crucial and challenging task in the Internet of Everything. In this communication, we propose and demonstrate the implementation of two amplitude-reconfigurable metasurfaces, loaded with PIN diodes and operated around 5.8 GHz, to develop a novel polarization-insensitive backscatter communication system. Both communication transmitters using uniform and gradient metasurfaces are able to achieve high-efficiency polarization conversion, and thus can cooperate with the receiver end to mitigate the interference generated by environmental carrier sources. To achieve directional information transmission, we design a corner reflector metasurface and a gradient metasurface, which respectively enhances the gain in the backward and fixed scattering directions. Due to advantages of polarization insensitivity and low energy consumption, this system promises great potential for future engineering applications in the Internet of Things, particularly for smart home, wearable sensing and other related areas.