Orbit Determination and Thrust Estimation for Noncooperative Target Using Angle-Only Measurement

Author:

Zhang Zhixun12,Shu Leizheng23,Zhang Keke12,Zhu Zhencai12,Zhou Meijiang1,Wang Xinwei4,Yin Weidong23

Affiliation:

1. Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 200120, China.

2. University of Chinese Academy of Sciences, Beijing 100049, China.

3. Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China.

4. School of Engineering and Materials Science, Queen Mary University of London, E1 4NS London, UK.

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

General Medicine

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