The properties of neutron and proton ¹S₀ superfluidity are studied within the relativistic mean field and the Bardeen–Cooper–Schrieffer theories by taking the effects of K⁻ and |$\bar{K}^{0}$| condensations into account in neutron star matter without the hyperon degrees of freedom. It is found that antikaon condensations change the Fermi momenta, the effective masses and the single particle energies of nucleons in neutron star matter. These changes lead to a strong suppression of the neutron ¹S₀ superfluidity and an obvious enhancement of the proton ¹S₀ superfluidity in neutron star matter, respectively. In particular, the neutron and proton ¹S₀ pairing gaps are gradually shrinking with the optical potential of antikaons from −80 to −130 MeV. And antikaon condensations have little influence on the neutron ¹S₀ superfluid range, however, they have been markedly downsized the proton ¹S₀ superfluid range as the deepening of the optical potential of antikaons in neutron star matter. We also found that the nucleon ¹S₀ superfluidity and K⁻ condensations within the scope of above optical potential of antikaons can occur in the core of PSR J1614-2230 and PSR J0348+0432 at the same time. Whereas |$\bar{K}^{0}$| condensations only occur in the two pulsars when the range of optical potential of antikaons is from −100 to −130 MeV.

Wide-field telescopes with long exposure times have stronger space target detection capabilities. However, complex background sky conditions will still cause a series of difficulties in detecting space debris, such as a large number of star points, a large amount of noise, and the discontinuity and nonlinearity of the target. We propose a space debris automatic extraction channel with a high detection rate and low computational cost to solve these difficulties. We apply an improved median filter for noise elimination and then the double-structure morphological filter algorithm used to suppress the background of the star image to eliminate star points and noise. Then, the guided filter was used to eliminate residual noise, and star points were used to reduce the impact on the target. Finally, the improved Hough transform was also applied to detect the target in the image. Our automatic extraction algorithm is used in real astronomical star maps, including different orbiting satellites (star-tracking mode). These images were obtained by using a 280 mm diameter telescope, which was located in Changchun Observatory. The experimental results demonstrated the effectiveness of the extraction algorithm in this study. It can effectively detect and track space targets in a long-exposure wide-field surveillance system and has high positioning accuracy and low computational complexity, which solves the problem of space debris extraction under a complex background.

In the past few years, the increasing amount of space debris has triggered the demand for distributed surveillance systems. Long exposure time can effectively improve the target detection capability of the wide-area surveillance system. Problems that also cause difficulties in space-target detection include large amounts of data, countless star points, and discontinuous or nonlinear targets. In response to these problems, this paper proposes a high-precision space-target detection and tracking pipeline that aims to automatically detect debris data in space. First, a guided filter is used to effectively remove the stars and noise, then Hough transform is used to detect space debris, and finally Kalman filter is applied to track the space debris target. All experimental images are from Jilin Observatory, and the telescope is in star-tracking mode. Our method is practical and effective. The results show that the proposed automatic extraction channel of space debris can accurately detect and track space targets in a complex background.

Space target detection is the core technology of space surveillance system. The large field of view telescope has strong space detection capabilities, and its realization also faces many challenges. We propose an automatic extraction algorithm for space debris, aiming to automatically extract information about space targets. Our method is mainly divided into three parts. In the first stage, image denoising processing is carried out for various noise interference in the image. The proposed wavelet transform and total variational hybrid filtering algorithm are applied to eliminate noise, which reduces the impact of noise on target detection and greatly retains target information. In the second stage, we propose an improved morphological operator to eliminate uneven background. The third stage uses Hough transform to obtain candidate debris targets. These images were taken during an observation campaign, the observatory is located in Jilin. Experimental results show that the target detection algorithm proposed in this paper can effectively extract space target information and solve the problem of space target detection for large-field telescopes.

A single electro-optical (EO) sensor used in space debris observation provides angle-only information. However, space debris position can be derived using simultaneous optical measurements obtained from two EO sensors located at two separate observation sites, and this is commonly known as triangulation. In this paper, we propose a new triangulation algorithm to determine space debris position, and its analytical expression of Root-Mean-Square (RMS) position error is presented. The simulation of two-site observation is conducted to compare the RMS positioning error of the proposed triangulation algorithm with traditional triangulation algorithms. The results show that the maximum RMS position error of the proposed triangulation algorithm is not more than 200 m, the proposed triangulation algorithm has higher positioning accuracy than traditional triangulation algorithms, and the RMS position error obtained in the simulation is nearly consistent with the analytical expression of RMS position error. In addition, initial orbit determination (IOD) is carried out by using the triangulation positioning data, and the results show that the IOD accuracy of two-site observation is significantly higher than that of the single-site observation.

Initial orbit determination (IOD), as a basis for initial orbit association and accurate orbit determination (OD), has a crucial role in the process of obtaining space debris orbit information. Among the traditional methods, the Gooding method has better convergence and stability. In this study, the Gooding method is enhanced to solve the issues discovered. A novel initial orbit determination (IOD) method is developed using the proposed improvement measures of the single-parameter initial value determination (SIVD) method, the fitted-curve noise suppression method, the restricted corrective value solution method, the removal of trivial solutions, etc. The experimental results verify the effectiveness of the improved method. The success rate of the initial orbit determination reached 99%, and the accuracy of the solved orbit parameters was significantly improved, especially the semi-major axis (SMA) error of less than 50 km, accounting for 88% of the total. It can be seen that the method meets the demand of space-based space debris cataloging for initial orbit association and can serve in the field of space situational awareness, which has important practical significance and application potential.