In many cases, we have few optical observations over a short time span, and most of the information generated is too limited to compute a full orbit according to the angles-only method. This study aims to develop a mathematical model to determine the precise orbit from the optical observation data by the least squares method. We have used a set of the Global Navigational Satellite Systems, which are tracked by the Optical Satellite Tracking Station (OSTS) at the National Research Institute of Astronomy and Geophysics (NRIAG), Egypt, to access high-quality predictions for the orbits. We analyzed the orbit predictions from the observations of these satellites that are tracked from seven world stations using the laser ranging method, and the obtained results are compared with orbital elements produced using the Two-Line Element (TLE). The results showed that the orbital prediction accuracy differs for optical observations from laser observations because of the inaccuracy of the NORAD catalog information used; this is due to the difference between the time of observation and the epoch time of TLE.

In an effort to accomplish the real-time acquisition of the laser ranging results of space debris during the daylight and enhance the observation success rate, this paper establishes a joint distribution model of noise and echo signals grounded on the intensity distribution law of the daylight background noise. Through an in-depth analysis of the measurement characteristics of single-photon detectors, a real-time recognition algorithm based on the disparity in statistical distribution is put forward. This algorithm partitions the observation data into intervals of equal length. It then employs the goodness-of-fit test of the geometric distribution to ascertain the data distribution law. Subsequently, it locates the interval in which the echo signal resides by analyzing the contribution degree of the chi-square statistic. The experimental outcomes indicate that under the circumstances of a laser frequency of 400 Hz and a background noise photon rate of 2 × 10⁷ photons per second, the algorithm is capable of achieving real-time recognition of the echo interval for an intensity of 0.09 echo photons per single pulse within 1 s. This breakthrough resolves the critical challenge of daylight echo discrimination in high-noise environments. This method overcomes the constraints of the traditional signal intensity threshold and offers a novel technical approach for the tracking and precise orbit determination of space debris in a low signal-to-noise ratio environment.

As one of the major error sources, satellite signature effect should be reduced or even erased from the distribution of the post-fit residuals to improve the ranging precision. A simulation of satellite signature effect removal process for normal point algorithm is conducted based on a revised model of satellite response, which fully considers the structural and distribution characteristics of retroreflectors. In order to eliminate both long-term and short-term satellite signature effect, a clipping method for SLR data processing is proposed by defining the clipping location as 5.6 mm away from the mean value of the long-term fit residuals to select effective returns for normal points. The results indicate that, compared to normal points algorithm, the RMS per NP of LAGEOS-1 observation data processed by the clipping method is reduced from 62.90 ± 9.9 mm to 56.07 ± 4.69 mm, and the stability of RMS is improved 53%. This study improves the satellite signature effect model and simulates the fluctuation of normal points caused by satellite signature effect for the first time. The new method based on the simulation of satellite signature effect has stronger robustness and applicability, which can further minimize the influence of satellite signature effect on the SLR production and significantly improve the data property.

Observation task planning is a key issue and the first step in the development of automated Satellite Laser Ranging (SLR) systems. Aiming at the problem of dynamic change of cloud cover during SLR operation, this paper proposes an autonomous mission planning algorithm for SLR based on the Rolling Horizon Optimization (RHO) framework. A hybrid event- and cycle-driven replanning mechanism is adopted, and four functional modules, rolling, planning, information acquisition and decision-making, are established to decompose the SLR observation task planning process into a series of static planning intervals. An improved ant colony algorithm is proposed and utilized to realize the autonomous planning of SLR system observation tasks, and the above autonomous planning algorithm is verified and analyzed based on the SLR system at station 7237. The results show that the above algorithm can effectively increase the number of observation satellites and revenue under cloud disturbance, solve the problems of low efficiency and poor interference resistance of conventional static algorithms, and provide a new research idea for the establishment of an unattended SLR system.

The high-precision motion state analysis of space targets has important scientific value and application potential in the fields of geodynamics, geodesy, space collision warning and avoidance, and the capture, recovery, and removal of space debris. With the increasing repetition rate of satellite laser ranging systems, the inversion and analysis of space target motion state based on high-precision and high-repetition-rate satellite laser ranging data has become a hot spot in current research. How to filter out noise and retain valid information from the high-repetition-rate, high-precision laser ranging data has become a challenge. The traditional polynomial fitting method has problems with low data accuracy and erroneous deletion of valid data when processing laser ranging data with complex patterns. To address this problem, this paper innovatively designs a new type of spline function and accordingly proposes a laser ranging data preprocessing method that can automatically adapt to the trend of data variation. The method is validated using various characteristic observation data provided by Changchun station (7237), and the results show that the novel spline method is superior to the traditional method in maintaining the integrity of data patterns and significantly improves the data accuracy. After two iterations of denoising, the RMS of the novel spline method is reduced to one-fourth of that before denoising and as low as one-eighteenth of that of the traditional method, and the accuracy is further improved with the increase of the number of iterations. This study provides a practical and reliable novel solution for the preprocessing of laser ranging data with complex patterns.

Etalon is considered to be one of the most promising satellites for studying crustal motions, Earth rotation, and other scientific applications. Unfortunately, its outsized shape and signature result in a measurement range bias of several millimeters. On the basis of simulations of the echo signals, we analyze the center of mass corrections (CoM) for Etalon due to variations in satellite signature effect at different incidence angles. To minimize range bias caused by satellite signature effects, a center of mass corrections filter has been proposed for the processing of standard SLR data. According to the relationship between RMS of CoM and the upper limits of the rejection criteria, the measurements with the lowest variability of CoM are selected for normal points. Statistics indicate that the center of mass corrections filter can improve the stability of the collected data by 79%, and reduce the mean RMS of normal points from 163.7 × 41.8 ps to 118.2 × 8.94 ps. Additionally, the new algorithm is applicable to Etalon-2. In particular, this paper enriches and provides a useful reference for minimizing the effects of satellite signatures on the production of SLR data by providing a theoretical model that incorporates systematic errors in SLR.