To improve the detection efficiency of a long-distance dim point target based on dynamic programming (DP), this paper proposes a multi-frame target detection algorithm based on a merit function filtering DP ring (MFF-DPR). First, to reduce the influence of noise on the pixel state estimation results, a second-order DP named the MFF-DP is proposed. The current states of pixels on an image plane are estimated by maximizing the addition of the merit functions of the previous two frames and the observation data of the current frame. In addition, to suppress the diffusion of the merit function, the sequential and reverse observation data are connected in a head-to-tail manner to form a ring structure. The MFF-DP is applied to the ring structure, and the merit function of the MFF-DPR is obtained by averaging the merit functions of the sequential and reverse MFF-DPs. Finally, the target trajectory is obtained by correlating the extreme points of the merit functions of the MFF-DPR. The simulation and analysis results show that by merely adding a ring structure, the detection probability of the traditional DP can be improved by up to 40% when detecting point targets under the SNR of 1.8. The point target detection algorithm based on the MFF-DPR can achieve significantly better performance in point target detection compared with the traditional DPs with or without a ring structure. The proposed algorithm is suitable for radars and infrared point target detection systems.

<!-- *** Custom HTML *** --> To detect small infrared targets under the condition of dense clutters, we propose a single-frame target detection algorithm based on a small bounding-box filter, which is characterized by good adaptability to the position and size of a small target. During the small target detection process, the proposed algorithm first searches for the local maximum gray pixel and then, a set of concentric bounding boxes whose center is the pixel found in the first step is constructed, and the detection thresholds of a neighboring region of this pixel are calculated based on the bounding boxes. Finally, the minimum threshold is used to detect small target pixels in the neighboring region. A fast version of the proposed algorithm is a minimum bounding-box filter, which can be implemented by dividing an image into blocks and using the mid-range and range to assess the concentration trend and dispersion of the background. Simulation and analysis results show that the proposed algorithm can achieve high detection probability and low false alarm rate when detecting small targets in the complex background; while its fast version has high computational efficiency. The proposed algorithm can be used in infrared searching and tracking systems.

<!-- *** Custom HTML *** --> We propose a 3D Hough transform (3D-HT) algorithm that can overcome the disadvantages of high complexity and large data storage space of the existing 3D-HT-based small target detection algorithms. The proposed algorithm uses two coordinates at different times and coordinate errors to construct a 3D pipeline. Subsequently, it counts the number of points in the 3D pipeline and confirms the presence of a target trajectory in the pipeline when the number of points exceeds a predefined threshold. Finally, it performs trajectory merging and filtering before outputting the target trajectory coordinates. The proposed algorithm has low complexity because the used trajectory parameters are the coordinates in the data space, and only a linear transform between the coordinates is required. Unlike the existing algorithms that use an accumulator array to represent the Hough space, the proposed algorithm uses only a single position-adaptive cumulative cell in the Hough space. Therefore, there is no limitation on data storage in the Hough space. Simulation and analysis show that the small target detection algorithm based on the proposed transform is robust to noise, requires small data storage space, and has high computational efficiency. The proposed algorithm can be used in infrared and radar small target trajectory detection systems.

Aiming to address the chicken-and-egg problem in star identification and the intrinsic parameter determination processes of on-orbit star sensors, this study proposes an on-orbit self-calibration method for star sensors that does not depend on star identification. First, the self-calibration equations of a star sensor are derived based on the invariance of the interstar angle of a star pair between image frames, without any requirements for the true value of the interstar angle of the star pair. Then, a constant constraint of the optical path from the star spot to the center of the star sensor optical system is defined to reduce the biased estimation in self-calibration. Finally, a scaled nonlinear least square method is developed to solve the self-calibration equations, thus accelerating iteration convergence. Our simulation and analysis results show that the bias of the focal length estimation in on-orbit self-calibration with a constraint is two orders of magnitude smaller than that in on-orbit self-calibration without a constraint. In addition, it is shown that convergence can be achieved in 10 iterations when the scaled nonlinear least square method is used to solve the self-calibration equations. The calibrated intrinsic parameters obtained by the proposed method can be directly used in traditional star map identification methods.

This paper proposes an all-sky star map identification algorithm that can simultaneously achieve high identification probability, low algorithm complexity, and small databases for well photometric and intrinsic parameters-calibrated star sensors. The proposed algorithm includes three main steps. First, a binary radial pattern table is constructed offline. Then, the maximum value matching of the radial pattern is performed between the star spots and the guide stars, and the star pairs (i.e., the minimum star map) after radial pattern matching undergo a coarse matching through angular distance cross-validation. Finally, a reference star map is designed based on the identified star pairs, and the matching of all the star spots in the field of view is realized. Simulation and analysis results show that the database required by the proposed algorithm for 5,000 guide stars is not larger than 200 KB. Also, when false and missing star spots account for 50% of all guide stars and the star spot extraction error is 0.5 pixel (the corresponding pointing error is 26″), the average star map identification time of the proposed algorithm is less than 2 ms, and its identification probability is higher than 98%. The results demonstrate that the proposed algorithm performs better than similar algorithms.

In the conventional methods of star sensor accuracy evaluation, neither the accuracy calculation formula nor the Monte Carlo method can comprehensively reflect the attitude measurement accuracy of star sensors in real-time. In this paper, a real-time analysis and calculation model for the attitude measurement accuracy of star sensors is proposed. Firstly, the basic attitude measurement model of the star sensor is established through the pinhole imaging model. Moreover, a moving frame of star sensor attitude measurement is established to avoid the defects of attitude representation by the Euler angle in the star sensor accuracy analysis. Then, combined with the error transfer of implicit overdetermined equations proposed in this paper, the error transfers of starspot extraction random error, star catalog random error, and intrinsic parameter system error are provided. Finally, simulation and field experiments are conducted to verify the accuracy of the proposed error analysis theory. The experimental results show that the attitude measurement accuracy of star sensors can be accurately estimated by using single-frame data of guide stars.