This paper describes the multilayer voting algorithm, a novel autonomous star identification method for spacecraft attitude determination. The proposed algorithm includes two processes: an initial match process and a verification process. In the initial match process, a triangle voting scheme is used to acquire candidates of the detected stars, in which the triangle unit is adopted as the basic voting unit. During the identification process, feature extraction is implemented, and each triangle unit is described by its singular values. Then the singular values are used to search for candidates of the imaged triangle units, which further improve the efficiency and robustness of the algorithm. After the initial match step, a verification method is applied to eliminate incorrect candidates from the initial results and then outputting the final match results of the imaged stars. Experiments show that our algorithm has more robustness to position noise, magnitude noise, and false stars than the other three algorithms, the identification speed of our algorithm is largely faster than the geometric voting algorithm and optimized grid algorithm. However, it takes more memory, and SVD also seems faster.

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications.

A full-sky autonomous star identification algorithm aimed at solving the “lost-in-space” problem is presented in this paper. It mainly consists of two steps: an initial match step and a reliability evaluation step. Oriented singular value feature matching is adopted to search for corresponding candidates of the stars detected in the initial match. After obtaining the stars' initial match results, an evaluation method is applied to estimate the reliability of candidates from the star voting results, acquiring the final unique matching of stars in the image. Experiments show that our algorithm is more robust to star position noise and magnitude noise than the two conventional algorithms. In the simulations, our algorithm achieves an identification rate of 97.0% with 2-pixel star position noise and 0.3 Mv star magnitude noise, and also performs well with false stars in the field of view. In addition, the memory requirement and identification time of our method are acceptable for actual engineering projects.

As an important error in star centroid location estimation, the systematic error greatly restricts the accuracy of the three-axis attitude supplied by a star sensor. In this paper, an analytical study about the behavior of the systematic error in the center of mass (CoM) centroid estimation method under different Gaussian widths of starlight energy distribution is presented by means of frequency field analysis and numerical simulations. Subsequently, an optimized extreme learning machine (ELM) based on the bat algorithm (BA) is adopted to predict the systematic error of the actual star centroid position and then compensate the systematic error from the CoM method. In the BA-ELM model, the input weights matrix and hidden layer biases parameters are encoded as microbat’s locations and optimized by utilizing the strong global search capacity of BA, which significantly improves the performance of ELM in terms of prediction accuracy. The simulation result indicates that our method can reduce the systematic error to less than 3.0 × 10⁻⁷ pixels, and its compensation accuracy is two or three orders of magnitude higher than that of other methods for estimating a star centroid location under a 3 × 3 pixel sampling window.

Imaging correlography, an effective method for long-distance imaging, recovers an object using only the knowledge of the Fourier modulus, without needing phase information. It is not sensitive to atmospheric turbulence or optical imperfections. However, the unreliability of traditional phase retrieval algorithms in imaging correlography has hindered their development. In this work, we join imaging correlography and ptychography together to overcome such obstacles. Instead of detecting the whole object, the object is measured part-by-part with a probe moving in a ptychographic way. A flexible optimization framework is proposed to reconstruct the object rapidly and reliably within a few iterations. In addition, novel image space denoising regularization is plugged into the loss function to reduce the effects of input noise and improve the perceptual quality of the recovered image. Experiments demonstrate that four-fold resolution gains are achievable for the proposed imaging method. We can obtain satisfactory results for both visual and quantitative metrics with one-sixth of the measurements in the conventional imaging correlography. Therefore, the proposed imaging technique is more suitable for long-range practical applications.

High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. In batch processing, we use high-performance parallel-computing on the graphics processing unit (GPU) for higher efficiency and lower operation time. In addition, a parallel processing mechanism is designed for our parallel algorithm to obtain higher performance. At the same time, other solving algorithms for the modern spectrum estimation model are introduced for discussion and comparison. We compare traditional high-resolution solving algorithms running on the central processing unit (CPU) and the parallel algorithm on the GPU for processing the interferogram. The experimental results illustrate that runtime is reduced by about 70% using our parallel solution, and the GPU has a great advantage in processing large data and accelerating applications.