Xin Wei

Endpoint localization of faint streak-like objects is an important component of space situational awareness. In this study, a correlation-based endpoint localization is proposed. It is composed of coarse localization and fine localization. The mathematical model of correlation is derived and verified, and it is used to select the optimal reference image of endpoints in different localization stages and conditions. Meanwhile, a correlation-coefficient-weighted centroid mapping is proposed to achieve the fine location. Experiments demonstrate that the proposed method achieves superior localization accuracy for faint streak-like objects in single-frame star images while maintaining practical computational efficiency, and maintains robust performance for a peak SNR ≥ 2. Furthermore, validation on real star images confirms the method’s validity and expected performance in practical operation.

The challenging task of detecting dim and small object in astronomy optical image sequences is to distinguish it from a large number of stars, and different kinds of noise. To address this issue, dim and small objects are tracked during imaging, and their signal is enhanced as point-like objects, while stars appear as streaks. We propose a transform-based feature accumulative enhancing (Trans-FAE) method for efficient dim and small object detection. First, the image is preprocessed to eliminate various types of noise, including hot pixels and nonuniform background, and a bidirectional one dimension (1D) iterative method is proposed to eliminate CCD smear effect. Then, Fourier and Radon transforms are used to estimate parameters of stellar streaks, and the iterative matched filtering and multi-frame stacking methods are implemented for star removal and dim and small object detection, which can generate stellar streak templates, and improve the signal-to-noise ratio (SNR) of objects. Finally, an adaptive threshold segmentation method is used to achieve object segmentation and detection. The experimental results show that the proposed method can detect objects successfully with SNR as low as 1, and without false alarms.

Full-sky autonomous star identification is one of the key technologies in the research of star sensors. As one of the classical pattern-based star identification methods, the Grid algorithm has shown promising performance. Na further modified it to improve robustness to position noise. However, the inherent alignment star mismatch and pattern inconsistency are still not solved. To address these problems, we propose a novel star identification method. Specifically, we design distance-guided redundant-coded patterns for different alignment stars to alleviate the problem of alignment star mismatch. Then, we create a masked grid pattern to address the inconsistency between the sensor pattern and the catalog pattern. Distances of the reference stars to their corresponding alignment stars are adopted to assist in choosing the correct alignment star, as well as reducing the number of catalog patterns that need to be evaluated. Experimental results on both synthesized and night sky images show that the proposed algorithm is quite robust to false stars, position noise, and magnitude noise. The identification accuracy of this algorithm is 98.43% with standard deviations of position noises is 2.0 pixels and 98.52% with standard deviations of magnitude noises is 0.5 Mv. Moreover, the algorithm obtains an average identification accuracy of 99.6% from night sky images.

With the development of high-volume manufacturing for very-large-scale integrated circuits, the purity of the light source in the extreme ultraviolet lithography (EUVL) system needs to fulfil extreme requirements in order to avoid thermal effect, optical distortion and critical dimension errors caused by out-of-band radiations. This paper reviews the key technologies and developments of the spectral purity systems for both a free-standing system and a built-in system integrated with the collector. The main challenges and developing trends are also discussed, with a view towards practical applications for further improvement. Designing and manufacturing spectral purity systems for EUVL is not a single task; rather, it requires systematic considerations for all relevant modules. Moreover, the requirement of spectral purity filters drives the innovation in filtering technologies, optical micromachining and advanced metrology.