Subpixel centroid estimation is the most important star image location method of star tracker. This paper presents a theoretical analysis of the systematic error of subpixel centroid estimation algorithm utilizing frequency domain analysis under the consideration of sampling frequency limitation and sampling window limitation. Explicit expression of systematic error of centroid estimation is obtained, and the dependence of systematic error on Gaussian width of star image, actual star centroid location and the number of sampling pixels is derived. A systematic error compensation algorithm for star centroid estimation is proposed based on the result of theoretical analysis. Simulation results show that after compensation, the residual systematic errors of 3-pixel-and 5-pixel-windows' centroid estimation are less than 2×10 −3 pixels and 2×10 −4 pixels respectively. star tracker, star image location, subpixel centroid estimation, centroid algorithm, frequency domain analysis, systematic error compensation Citation:Jia H, Yang J K, Li X J, et al. Systematic error analysis and compensation for high accuracy star centroid estimation of star tracker.

This paper studies the effects of the obstacle on non-line-of-sight ultraviolet communication links using multiple-scatter model based on a Monte Carlo method. On the condition that transmitter beam and receiver FOV just pass the top of the obstacle, and ranges is fixed, the received energy density is at its maximum. The path loss increases when the transmitter or the receiver is much near to the obstacle, because the nearby common scattering volumes decrease intensively. The optimal received range decreases with the increasing of the distance between transmitter and obstacle. The predictions are validated with experimental measurements. This work can be used for the guidance of UV system design and network technology to apply in complex surroundings, such as mountain, buildings, etc.

In most spacecraft, there is a need to know the craft's angular rate. Approaches with least squares and an adaptive Kalman filter are proposed for estimating the angular rate directly from the star tracker measurements. In these approaches, only knowledge of the vector measurements and sampling interval is required. The designed adaptive Kalman filter can filter out noise without information of the dynamic model and inertia dyadic. To verify the proposed estimation approaches, simulations based on the orbit data of the challenging minisatellite payload (CHAMP) satellite and experimental tests with night-sky observation are performed. Both the simulations and experimental testing results have demonstrated that the proposed approach performs well in terms of accuracy, robustness, and performance.

An analytical model of non-line-of-sight (NLOS) single-scatter propagation is presented that has no integral form and is intended for performance analysis and system design of NLOS UV communication. Based on isotropic scattering and a continuous wave transmitter, the analytical model is verified by the current NLOS single-scatter propagation model, with consistent results. Several rules concerning NLOS UV communication are put forward on the basis of this analytical model, which are shown as follows: on condition that the minimum single-scatter optical depth is less than 0.1, the path loss factor should be 1; to maintain the NLOS UV communication link, the transmitter needs to radiate neither a continuous wave nor a huge pulse but a low-power wave whose duration is approximately the duration of impulse response; the "best" extinction coefficient is approximately the inverse ratio of the efficient single-scatter range; on condition that the radiation intensity of the transmitter is fixed, the half field of views (FOVs) are positive factors, while the elevation angles are negative factors; on condition that the power of the transmitter is fixed, the conclusions mentioned above remain valid with the exception that the half FOV of the transmitter is a negative factor. These rules also apply to anisotropic scattering.