In space observation, star maps contain a large number of stars and noise, whose characteristics are similar to those of the targets that need to be detected. Traditional methods struggle to ensure both high efficiency and accuracy simultaneously. This paper proposes a space target detection method based on frame difference and target characteristics. Firstly, the star map is registered, then the background is suppressed using frame difference method and noise interference is reduced by Gaussian blurring. Finally, a multi-scale local target characteristic algorithm is used to calculate three feature parameters of suspected regions to further screen the targets. Experimental results show that the proposed algorithm significantly reduces false alarms while ensuring correct detection rates, and its speed is significantly improved. The algorithm fully utilizes the advantages of the frame difference method and the multi-scale local target characteristic algorithm, thus improving the detection efficiency and accuracy.
The attitude parameter is an important state parameter for a long axisymmetric target. The plane intersection (PI) method is a commonly used method for attitude estimation. However, this method only uses planes’ information in the object space under a multiple camera system (more than two cameras simultaneously observing a target). We propose two methods to address the aforementioned issue. One method involves minimizing the square of the object-space angle residual (OAR) and the other method involves minimizing the square of the image-space angle residual (IAR). The linear optimization method is used for the above minimizing problems. The simulation results demonstrate that the IAR method has higher accuracy than the PI and OAR methods under multiple and dual camera systems because it incorporates information of a pair of corresponding image points. Furthermore, our experiments have shown that the linear method is generally faster, and it has an equivalent accuracy compared to the iterative method.
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