The Shortest Distance Path (SDP) problem is a critical routing issue in communication networks, particularly in satellite networks. Typically, SDP is solved by graph-based iterative algorithms, while an explicit or analytic approach is challenging. However, considering the orbit dynamics and topology regularity, this paper proposes, for the first time, an explicit analytic phase-based algorithm STEPCLIMB to directly solve the SDP in low-Earth orbit (LEO) satellite networks. Based on the relationship between satellite phase and inter-satellite link distance, the SDP is modeled with the satellite phase, and SDP problem is converted into a total phase offset problem through theoretical derivations. Then STEPCLIMB is derived in two cases, respectively. Monte-Carlo simulations verify STEPCLIMB’s accuracy, which has zero error in the mono-valley case and has less than 0.1% error in the bi-valley case. The algorithm performs better in larger-scale constellations and can save over 99.4% computational cost compared to Dijkstra algorithm. Further, the SDP pattern and features in Starlink constellation are analyzed. The model proves that most inter-plane hops in the SDP occur successively, and the simulations further indicate that these hops prefer satellites in the higher latitude regions.

Mega-constellation networks widely employ inter-satellite links (ISLs) to enhance network connectivity. However, the ISLs, particularly the inter-plane ISLs that span different orbital planes, are dynamic and difficult to maintain. The inter-plane ISLs are less used and can be partly saved. Recently, a special ISL configuration with pruned inter-plane ISLs has been implemented in Starlink, yet less studied. This paper delves into the topological characteristics and routing issues of this pruned ISL pattern, specifically the 3-ISL topology. This topology is mathematically modeled and indexed, and key routing properties are derived, including the minimum hop-count, network diameter, network cost, and average path length of the 3-ISL topology. These fundamental properties are rigorously proven for both Mesh and Torus topologies. Additionally, the shortest path searching region and path diversity are deduced. Numerical simulations verify these propositions, and the results of mega-constellation scenarios suggest that pruning ISLs could lead to increased path distance, higher delay, and reduced network capacity. However, in certain constellation configurations, some metrics are comparable to those of the traditional 4-ISL topology, thereby potentially reducing system costs. Constellations with fewer orbital planes and more satellites in each plane are less affected by pruning ISLs and thus preferred. The findings can provide analytic insights for routing strategies and guide the integrated design of constellation configuration and ISL topology.

The safe proximity to spacecraft is a prerequisite for most on -orbit service missions. However, performing proximity operations on orbit is dangerous and high cost. Therefore, preverification of the proximity control algorithm through ground experiments can determine the reliability and promote the success of actual on -orbit missions. This article constructs an air floating experiment system and a guidance algorithm for the safe proximity to a complex-shaped tumbling spacecraft. The proposed control algorithm consists of two parts: 1) one part is an improved Gaussian mixture model, with which a novel artificial potential function is designed to accurately describe the complex shape of the target and provide three-dimensional collision avoidance constraints and 2) the other part is to use the fixed time control method to ensure that the service spacecraft can reach the desired position within a fixed time. The numerical simulation and experiments are implemented to verify the effectiveness of the proposed algorithm.

The spatial target motion model exhibits a high degree of nonlinearity. This leads to the fact that it is easy to diverge when the conventional Kalman filter is used to track the spatial target. The Unscented Kalman filter can be a good solution to this problem. This is because it conveys the statistical properties of the state vector by selecting sampling points to be mapped through the nonlinear model. In practice, however, the measurement noise is often time-varying or unknown. In this case, the filtering accuracy of the Unscented Kalman filter will be reduced. In order to reduce the influence of time-varying measurement noise on the spatial target tracking, while accurately representing the a posteriori mean and covariance of the spatial target state vector, this paper proposes an adaptive noise factor method based on the Unscented Kalman filter to adaptively adjust the covariance matrix of the measurement noise. In this paper, numerical simulations are performed using measurement models from a space-based infrared satellite and a ground-based radar. It is experimentally demonstrated that the adaptive noise factor method can adapt to time-varying measurement noise and thus improve the accuracy of spatial target tracking compared to the Unscented Kalman filter.

The traffic prediction method for Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) provides prior conditions for addressing challenges in resource allocation and management of LEO satellite-based IoT. However, traditional methods rely on the pregeneration of terrestrial centers, leading to substantial computational complexity and delays when applying prediction results to LEO satellite-based IoT. Therefore, achieving autonomous LEO satellite-based IoT traffic prediction is essential. Unlike the spatiotemporal features of terrestrial IoT, LEO satellite-based IoT traffic is influenced by both satellite dynamics and terrestrial user behavior. Fortunately, real IoT traffic information can be shared between adjacent satellites through Intersatellite Links (ISL). Inspired by the above, this article proposes, for the first time, an autonomous LEO satellite-based IoT traffic prediction method. A spatiotemporal fusion neural network (SFNN) employing a four-layer hybrid neural network architecture is designed to accommodate the unique spatiotemporal features of LEO satellite-based IoT traffic. The simulation results demonstrate that the proposed method has low computational complexity and achieves a better prediction performance than the five baseline methods in LEO constellations. Further simulations demonstrate that the proposed method achieves higher accuracy in large-scale LEO constellations, and the algorithm remains applicable under ISL instability and satellite failure conditions.