A multi-satellite co-tracking method for a single space target based on adaptive distributed spherical simplex information-weighted consensus filter. Issue 6 (15th March 2023)

• Record Type: Journal Article
• Authors: Hua, Bing; Yang, Guang; Wu, Yunhua; Chen, Zhiming
• Is Part Of: Advances in space research. Volume 71:Issue 6(2023)
• Page Start: 2640
• Page End: 2655
• Publication Date: 2023-03-15
• Subjects: Non-cooperative target -- Information filter -- Adaptive algorithm; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2022.11.035
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: A multi-satellite co-tracking method for a single non-cooperative target is proposed to extend the arc of observation as well as to improve the accuracy of tracking. Firstly, the motion of the target is considered to be affected by J2 perturbation, and the model of multi-satellite co-tracking a single space target is designed with only measured angles. Then, a multi-satellite co-tracking method for a single space target based on an adaptive distributed spherical simplex information-weighted consensus filter (ADSSICF) is proposed. The spherical simplex is used to reduce the computational cost of the information filter, and to improve the efficiency of tracking. The angle-only measurement equation is linearized by the statistical linear regression method, and linearization errors are compensated for by updating measurement noise. By designing an adaptive consensus algorithm, only a small number of iterations are needed to achieve network consensus, to improve the convergence speed of the consensus algorithm, and prove the stability of ADSSICF. Finally, a simulation of four low-orbit satellites tracking a single space target is established. The focus of this paper is on the real-time performance and tracking accuracy of the multi-satellite co-tracking a single space target with angle-only based on ADSSICF. The performance of ADSSICF is verified from three indicators: the consensus and convergence of algorithm, the accuracy of state estimation.