Star sensors, as the most precise attitude measurement devices currently available, play a crucial role in spacecraft attitude estimation. However, traditional frame-based cameras tend to suffer from target blur and loss under high-dynamic maneuvers, which severely limit the applicability of conventional star sensors in complex space environments. In contrast, event cameras—drawing inspiration from biological vision—can capture brightness changes at ultrahigh speeds and output a series of asynchronous events, thereby demonstrating enormous potential for space detection applications. Based on this, this paper proposes an event data extraction method for weak, high-dynamic space targets to enhance the performance of event cameras in detecting space targets under high-dynamic maneuvers. In the target denoising phase, we fully consider the characteristics of space targets’ motion trajectories and optimize a classical spatiotemporal correlation filter, thereby significantly improving the signal-to-noise ratio for weak targets. During the target extraction stage, we introduce the DBSCAN clustering algorithm to achieve the subpixel-level extraction of target centroids. Moreover, to address issues of target trajectory distortion and data discontinuity in certain ultrahigh-dynamic scenarios, we construct a camera motion model based on real-time motion data from an inertial measurement unit (IMU) and utilize it to effectively compensate for and correct the target’s trajectory. Finally, a ground-based simulation system is established to validate the applicability and superior performance of the proposed method in real-world scenarios.

Traditional high-precision star trackers rely on long-focal-length optical configurations, which suffer from bulky volume and heavy weight, which restricts their application to small satellite platforms. Meanwhile, lightweight optical architectures inevitably introduce residual optical aberrations that distort star spot energy distribution and degrade centroid extraction accuracy. Targeting Petzval-type star trackers, this paper proposes an optical-algorithm collaborative lightweight design scheme. First, a coupling mathematical model between centroid positioning error and four asymmetric optical aberrations is established to quantify the error contribution hierarchy of coma, astigmatism, distortion, and lateral chromatic aberration. Second, an aberration dimensionality-reduction optimization method is proposed to selectively constrain precision-sensitive asymmetric aberrations; the conventional six-element Petzval lens group is simplified to a four-element lightweight configuration, reducing lens count by 30% and total system length from 27 mm to 21 mm. Finally, a three-stage joint compensation framework consisting of aberration residual identification, iterative centroid offset correction, and regularized Richardson–Lucy image restoration is constructed for on-orbit low-computation platforms. Ground star-map tests and on-orbit measurements demonstrate that the star centroid error is reduced from 0.098 pixels to 0.014 pixels after compensation, corresponding to an 85.7% improvement in localization precision. The 16 μm in-spot energy concentration rises from 76.2% to 86.5%, with full-field energy uniformity improved by 19.2%. Attitude measurement accuracy better than 3″ ($3\sigma$) is achieved for the X- and Y-axes (roll/pitch). The system exhibits favorable robustness under stray-light and multi-scene observation conditions. This integrated optical-algorithm solution realizes arcsecond-level attitude measurement for roll and pitch axes under strict lightweight constraints.