Star sensor based star tracking method

Abstract

The invention provides a star sensor based star tracking method. The method comprises steps as follows: firstly, angular velocity is estimated and filtered; then, star vectors are predicted and transformed into star points; finally, the star points are extracted from a wave gate with a prediction star point as the center. The method does not depend on attitude information, only the star vector of the star point of each frame is required to be modeled as a whole, and a star point predicted value is obtained on the basis of the star vector and a parameter of a star sensor. The method adopts a simple model and is small in computational amount and easy to implement; different from an existing star tracking method used for calculating angular velocity and recursion based on quaternion differential and adopting a guide star catalogue, the method has the advantages that the angular velocity can be directly computed on the basis of star vector differential for star vector recursion, the star tracking is further realized, and the method can be used as an effective complement to existing star tracking methods.

Description

technical field [0001] The invention relates to a star tracking method for a star sensor, in particular to a star tracking method for a star sensor based on star vector difference. Background technique [0002] The star sensor is the attitude sensor with the highest precision at present, and is widely used in satellite attitude determination. After capturing successfully throughout the day, the star sensor mainly works in star tracking mode. In the star tracking method, the star point prediction is carried out firstly, and a wave gate of a certain size is formed centering on the predicted star point position, and the image processing software extracts the star point position information from the wave gate, which significantly reduces the image processing time and improves the method efficiency. When the star point prediction error is large, the wave gate must be increased accordingly. On the one hand, the amount of calculation is increased. On the other hand, because the p...

AI Technical Summary

Problems solved by technology

The second type of method does not rely on external information for star point prediction. There are fewer studies on this type of method, mainly star point trajectory modeling

There are two shortcomings in this method: First, in the modeling of the star point trajectory, the prior information such as the maneuver frequency of the star point and the variance of the random acceleration is required, which is difficult to obtain. In fact, at different positions of the detector, the shape of the star track It is not the same, the star point acceleration is not the same, which is especially obvious in the large field of view star sensor and multi-head star sensor, so it is difficult to accurately model all star trajectories; secondly, for each star in the detection The trajectory on the controller needs to establish a three-dimensional motion model in the x direction and the y direction, and perform star point prediction and filtering on the basis of the model. In actual use, an average of 15 star points are used for each frame of star map to fix the pose, so it is necessary to Perform recursion and filtering on 15 star trajectory models respectively, which requires a large amount of calculation

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