The “defocused transmit-focused receive” (DTFR) mode in the distributed multiple-input multiple-output (MIMO) radar network is very effective in multitarget tracking. In this mode, a completely defocused beam is transmitted and a focused receive beam is synthesized so that the MIMO radar is capable of tracking targets independently. A joint target assignment and power allocation (TAPA) strategy is developed for multiple distributed MIMO radar networks in cluttered environment using the DTFR mode. Our aim is to achieve the better system tracking accuracy under the constraints of receive beam direction capability and power budget. We derive the posterior Cramer-Rao lower bound (PCRLB) and adopt it as the objective function, since it quantifies the precision of target state estimates. It is shown that the TAPA problem is a mixed integer programming and NP-hard problem, where two involved parameters, i.e., the target-radar assignment and power allocation, are both coupled in the objective and in the constraints. By introducing an intermediate variable, we propose an efficient two-step-based solution for solving this problem. The simulation results show the superior performance and adaptivity compared with existing algorithms.

The evolution of satellite surveillance technology, coupled with advanced on-board intelligent systems and improved attitude maneuver capabilities, has positioned mission scheduling and execution as a prominent and active research area in recent years. With the urgent need for mission scheduling and execution to evolve from the static ground target to the time-sensitive moving target, the existing satellite often yields unsatisfactory results when managed through conventional manual methods for continuously tracking a time-sensitive moving target. In response to this challenge, the paper introduces a reinforcement learning driven method enabling the satellite to schedule and execute the tracking of time-sensitive moving target autonomously. In the paper, we conduct a meticulous analysis of the target tracking process, which we decompose into three distinct sub-phases and address it utilizing attitude control, elucidating the integration of our model with satellite attitude dynamics and kinematics. Besides, we propose Flight Trajectory Prediction Module to predict the future trajectory position and minimize the position error due to the communication latency. With the dual aim of expeditiously tracking the target and concurrently enhancing payload stability, a well-designed reward function, which is applied to various reinforcement learning (RL) algorithms, effectively governs satellite attitude control to achieve accurate and stable tracking of moving target. In addition, through the eight distinct tracking scenarios simulated in our proposed Satellite Target Tracking Environment (STTE), our extensive experiments and ablation studies consistently underscore the advantages and efficacy of our proposed method. This work serves as a foundational cornerstone for future research endeavors focused on tracking multiple time-sensitive moving targets within extensive and complex intelligent satellite constellation.

Effective detection and tracking of dim and small targets with low signal-to-clutter ratio (SCR) has become a research hotspot due to its wide range of applications. However, most of the previously proposed methods seldom utilize the abundant temporal features formed by target motion, resulting in poor detection and tracking performance for low SCR targets. In this article, we analyze the difficulty based on spatial features and the feasibility based on temporal features of realizing effective detection. According to this analysis, we use a multi-frame as a detection unit and propose a detection method based on temporal energy selective scaling (TESS). Specifically, we investigated the composition of intensity temporal profiles (ITPs) formed by pixels on a multi-frame detection unit. For the target-present pixel, the target passing through the pixel will bring a weak transient disturbance on the ITP and introduce a change in the statistical properties of ITP. We use a well-designed function to amplify the transient disturbance, suppress the background and noise components, and output the trajectory of the target. Subsequently, to solve the contradiction between the detection rate and the false alarm rate brought by the traditional threshold segmentation, we associate the temporal and spatial features of the trajectory and propose a trajectory extraction method based on the 3D Hough transform. Finally, we propose a trajectory segments-based multi-target tracking method. Compared with the various state-of-the-art detection and tracking methods, experiments in multiple scenarios prove the superiority of our proposed methods for dim and small targets in low SCR.

Active fault-tolerant systems are crucial for ensuring the reliability and safety of spacecraft attitude control, especially when dealing with actuator faults that could jeopardize mission success. This article introduces a comprehensive framework for fault detection and identification (FDI) and fault-tolerant control (FTC) tailored to meet stringent real-time processing requirements and limited onboard resources. We propose a neural network (NN)-based fault identification approach, termed FaultNet, which leverages long short-term memory (LSTM) architectures to achieve millisecond-level inference ( $\approx3$ ms). Compared to traditional observer-based FDI methods with multisecond convergence times, FaultNet offers a lightweight solution (model size $\approx 550$ kB) suitable for onboard deployment, delivering rapid and precise fault estimation. It enables accurate identification of both constant and time-varying faults, including effectiveness loss and additive bias. For fault-tolerant attitude control in spacecraft, we develop a reinforcement learning (RL)-based controller, incorporating carefully designed state representations and reward functions to ensure robust performance. The proposed framework is rigorously evaluated across four customized fault scenarios—including both constant and time-varying faults—and achieves rapid convergence (average attitude alignment within 20 s) and sustained stability (average convergence duration exceeding 280 s). The controller effectively compensates for actuator faults, maintaining high control precision with minimal attitude oscillation and consistent recovery performance. Simulation outcomes validate the NN-based FDI’s efficiency and accuracy, as well as the RL-based FTC’s (RL-FTC’s) reliability, showcasing minimal fault estimation errors and consistent fault recovery performance. The source code and associated data are available at https://github.com/LUWENL/fault-tolerant-control

Small celestial body (SCB) exploration is a research hotspot in deep space exploration technology. However, the scarcity of small celestial body surface images severely hinders the development of probe intelligence systems, and it is challenging to redeploy probes to obtain massive images due to the high cost. In recent years, with the growth of deep learning, generative adversarial networks have shown excellent potential in generating real images. This paper presents a high-quality small celestial body surface image synthesis method SCB-GAN, which can generate a massive amount and large diversity of small celestial body surface images that match human perception from a limited dataset. Specifically, we propose MineAug, which can automatically select appropriate data augmentations from the given SCB surface imageset based on an efficient mining mechanism, improving the robustness of the GAN training process. We also add the vision-aided loss to our network to further improve image quality. Through extensive experiments, our method outperforms recent methods under a variety of metrics in terms of visual quality comparison, no-referenced image quality assessment, and human subjective evaluation, which has significant implications for the development of intelligent systems. The source code and associated data are available at https://github.com/LUWENL/SCB-GAN