The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

This study aims to analyze four different telescope pointing correction models to verify the highest accuracy of the laser ranging telescope corrected by the back propagation (BP) neural network model optimized by the proposed genetic algorithm and Levenberg–Marquardt. In this process, first, the observation data of 95 stars are used to solve the coefficients of the four models, and then the pointing accuracy of the telescope corrected by those four models is verified by the detection results of 22 stars. The results indicate that the pointing accuracy of the telescope corrected by the three traditional pointing correction models, the mount model, the spherical harmonic function model, and the basic parameter model, reaches approximately 15 in. in the azimuth and ∼10 in. in the pitch; however, the BP neural network model optimized by the genetic algorithm and Levenberg–Marquardt has a pointing accuracy of 3.42 in. in the azimuth and 2.44 in. in the pitch. Finally, different space debris is detected by the telescope corrected by this model. The results show that the pointing accuracy of the telescope corrected by this model probably increases to nine times in the azimuth and three times in the pitch. The results of this study prove that the BP neural network model optimized by the genetic algorithm and Levenberg–Marquardt greatly increases the pointing accuracy of the telescope and thus significantly improves the success rate of space debris detection.

The BeiDou global navigation satellite system (BDS-3) has already provided worldwide navigation and positioning services for which the high-precision BDS-3-predicting orbit is the foundation. The arc length of the observed orbits and the solar radiation pressure (SRP) are two important factors for producing precise orbit predictions. The contribution studies the influences of these factors on BDS-3 orbit prediction. Three-month data from 1 July 2021 to 30 September 2021 are used to analyze optimal arc lengths and different ECOM SRP models for obtaining precise BDS-3 orbit predictions. The results show that the best-fitting arc length for the BDS-3 MEO/IGSO satellite is 42–48 h by comparing the final precise ephemeris and SLR validation. Furthermore, the ECOM9 SRP model shows improved orbit-prediction accuracy than that of the ECOM5 SRP model when the satellites move in and out of the eclipse season. As for the ECOM9 SRP model, the user range error (URE) accuracy of 6 h orbit predictions when satellites are in and outside of the eclipse season is 0.036 m and 0.030 m, respectively. In addition, the orbit prediction accuracy of the BDS-3 satellites does not decrease significantly since BDS-3 satellites apply the continuous yaw-steering (CYS) attitude mode during the eclipse season.

Aiming at the problem that it is easy to fall into local minimum in the multi domain resource allocation process of wireless communication in the Internet of things, a multi domain resource allocation algorithm of wireless communication in the Internet of things based on chaotic neural network is proposed. The effects of attenuation factor and temperature fading parameters on the chaotic characteristics of chaotic neural network are analyzed, and the network parameters are selected reasonably. This paper obtains the multi domain resources of integrated Internet of things wireless communication, updates the multi domain resources, and builds a multi domain resource configuration model through relevant network parameters. In this paper, we use data mining method to obtain the multi domain resource data of wireless communication. And the parameters of the network are appropriately selected to make the neural network appear chaotic, so the resource allocation process based on the chaotic neural network is designed. Therefore, the resource allocation process based on chaotic neural network is designed. The experimental results show that the configuration results of the algorithm are consistent with the ideal configuration results, and the shortest end-to-end delay is 10 ms, and the lowest packet loss rate is 4%.