{"title":"Orbit determination and thrust estimation for non-cooperative target using angles-only measurement","authors":"Zhixun Zhang, Leizheng Shu, Keke Zhang, Zhencai Zhu, Meijiang Zhou, Xinwei Wang, Weidong Yin","doi":"10.34133/space.0073","DOIUrl":null,"url":null,"abstract":"The classical interactive multimodel (IMM) algorithm has some disadvantages in tracking a noncooperative continuous thrust maneuvering spacecraft, such as poor steady-state accuracy, difficult selection of subfilter parameters, and mismatched model jump. To address the abovementioned problems, a variable-dimensional adaptive IMM strong tracking filtering algorithm (VAIMM-STEKF) is proposed to estimate the spacecraft’s position, velocity, and maneuvering acceleration state. VAIMM-STEKF contains 2 models, model 1 and model 2, which correspond to the tracking of the spacecraft in maneuvering and nonmaneuvering situations. Model 1 estimates the position and velocity of the spacecraft to ensure tracking accuracy when no maneuver occurs. Model 2 is a strong tracking filter with an augmented state. The adaptive IMM algorithm adjusts the fixed Markov transfer matrix in real time according to the model output probability. According to the different states of the spacecraft, the corresponding model interactive fusion method, together with the strong tracking filter, is adopted to ensure fast tracking when the spacecraft state changes. This method can also adapt to continuous thrust maneuvering spacecraft with different orders of magnitude. Simulation results show that the position accuracy of VAIMM-STEKF can be improved by approximately 27% and the speed accuracy can be enhanced by approximately 17% under different levels of maneuvering acceleration compared with those of the IMM algorithm. The convergence speed of VAIMM-STEKF is also better than the IMM algorithm.","PeriodicalId":44234,"journal":{"name":"中国空间科学技术","volume":"7 1","pages":"0"},"PeriodicalIF":0.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国空间科学技术","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34133/space.0073","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, AEROSPACE","Score":null,"Total":0}
引用次数: 0
Abstract
The classical interactive multimodel (IMM) algorithm has some disadvantages in tracking a noncooperative continuous thrust maneuvering spacecraft, such as poor steady-state accuracy, difficult selection of subfilter parameters, and mismatched model jump. To address the abovementioned problems, a variable-dimensional adaptive IMM strong tracking filtering algorithm (VAIMM-STEKF) is proposed to estimate the spacecraft’s position, velocity, and maneuvering acceleration state. VAIMM-STEKF contains 2 models, model 1 and model 2, which correspond to the tracking of the spacecraft in maneuvering and nonmaneuvering situations. Model 1 estimates the position and velocity of the spacecraft to ensure tracking accuracy when no maneuver occurs. Model 2 is a strong tracking filter with an augmented state. The adaptive IMM algorithm adjusts the fixed Markov transfer matrix in real time according to the model output probability. According to the different states of the spacecraft, the corresponding model interactive fusion method, together with the strong tracking filter, is adopted to ensure fast tracking when the spacecraft state changes. This method can also adapt to continuous thrust maneuvering spacecraft with different orders of magnitude. Simulation results show that the position accuracy of VAIMM-STEKF can be improved by approximately 27% and the speed accuracy can be enhanced by approximately 17% under different levels of maneuvering acceleration compared with those of the IMM algorithm. The convergence speed of VAIMM-STEKF is also better than the IMM algorithm.