The integration of a star tracker and gyroscope units (GUs) can take full advantage of the benefits of each, and provide continuous and accurate attitude information with a high update rate. The systematic error calibration of the integrated system is a crucial step to guarantee its attitude accuracy. In this paper, a comprehensive calibration method for the star tracker and GUs integrated system is proposed from a global perspective. Firstly, the observation model of the predicted star centroid error (PSCE) with respect to the systematic errors including the star tracker intrinsic parameter errors, GUs errors and fixed angle errors is accurately established. Then, the systematic errors are modeled by a series of differential equations, based on which the state-space model is established. Finally, the systematic errors are decoupled and estimated by a Kalman filter according to the established state-space model and observation model. The coupling between the errors of the principal point and subcomponents of the fixed angles (i.e., ${\mathrm{\Psi }}_x$ and ${\mathrm{\Psi }}_y$ ) is analysed. Both simulations and experiments indicate that the proposed method is effective at estimating the systematic errors of the star tracker and GUs integrated system with high accuracy and robustness with respect to different star centroid accuracies and gyroscope noise levels.

The purpose of this work was to study bias error in acceleration-based transfer alignment, which is probably caused by cross-correlation between the dynamic lever-arm and the linear motion of a ship. A new prediction model for the cross-correlation-caused error is proposed in this paper. We adopt the Bernoulli-Euler Beam as a simplified ship hull-girder model to verify the existence of the cross-correlation. Then, the mathematical mechanism and the prediction model of the bias error are deduced via the ordinary least squares theory. Three factors influence the bias error in the prediction model: the amplitude of the dynamic lever arm acceleration, the amplitude of the ship motion acceleration, and the cross-correlation between them. Simulation experiments are then conducted to test the influence of the factors. The results show that the mechanism analysis is reasonable, and the bias error prediction model is in agreement with the experimental results. Thus, the proposed prediction model has the potential to deduce the bias error in high-accuracy transfer alignment.

Utilizing coordinated UAV formations for emergency disaster relief is a key future trend, but traditional evaluation methods have three major drawbacks: high computational complexity, heavy reliance on expert experience, and poor generalization in multi-scenario small-sample settings. To address these issues, this paper first designs a four-level evaluation index system that covers 5 core capabilities and targets 4 typical disaster relief scenarios. Next, it establishes an AHP model that quantifies the performance of 406 UAV formations, thereby providing high-quality labeled data for subsequent research. Furthermore, the paper constructs a ResNet + Atten deep learning network with a hybrid architecture, which improves both the self-learning ability of expert knowledge and the efficiency of multi-scenario evaluation. To solve small-sample overfitting and expert bias, the paper proposes a physically meaningful controllable perturbation data augmentation method: one that works by perturbing 23 UAV performance metrics within a 5–15% range to expand the sample size. Comparative experiments are conducted using three methods, BP neural networks, ResNet, and LSTM, and results show that ResNet + Atten achieves superior performance. Additionally, the data augmentation method effectively enhances the generalization ability of the model. The proposed method provides a reliable method for evaluating the performance of UAV multi-scenario collaborative disaster relief operations.