Inadequate geometric accuracy of cameras is the main constraint to improving the precision of infrared horizon sensors with a large field of view (FOV). An

Horizon-based optical navigation (OPNAV) is an attractive solution for deep space exploration missions, with strong autonomy and high accuracy. In some scenarios, especially those with large variations in spacecraft distance from celestial bodies, the visible horizon arc could be very short. In this case, the traditional Christian–Robinson algorithm with least-squares (LS) estimation is inappropriate and would introduce a large mean residual that can be even larger than the standard deviation (STD). To solve this problem, a simplified measurement covariance model was proposed by analyzing the propagation of measurement errors. Then, an unbiased solution with the element-wise total least-squares (EW-TLS) algorithm was developed in which the measurement equation and the covariance of each measurement are fully considered. To further simplify this problem, an approximate generalized total least-squares algorithm (AG-TLS) was then proposed, which achieves a non-iterative solution by using approximate measurement covariances. The covariance analysis and numerical simulations show that the proposed algorithms have impressive advantages in the short-arc horizon scenario, for the mean residuals are always close to zero. Compared with the EW-TLS algorithm, the AG-TLS algorithm trades a negligible accuracy loss for a huge reduction in execution time and achieves a computing speed comparable to the traditional algorithm. Furthermore, a simulated navigation scenario reveals that a short-arc horizon can provide reliable position estimates for planetary exploration missions.

With the extended operational time of micro-satellites in orbit, the statistical characteristics of process and measurement noise often deviate from their nominal values, leading to a decline in the performance of traditional extended Kalman filters (EKFs) for attitude estimation and gyro drift compensation. The adaptive EKF algorithm based on variational Bayesian theory requires the nominal process noise matrix to closely match the true values; otherwise, its accuracy is significantly compromised. To address this challenge, this paper proposes an enhanced adaptive multiplicative EKF (AMEKF) algorithm based on variational methods. Compared to the traditional AMEKF, the proposed algorithm processes the gyroscope’s measured angular velocity using total variation, effectively removing the two zero-mean Gaussian noises that correspond to the process noise in this context. By using the processed angular velocity for attitude and gyro drift estimation, the algorithm bypasses the uncertain process noise, resulting in improved accuracy compared to traditional algorithms. Simulation results demonstrate that, in comparison with the traditional AMEKF, the proposed algorithm reduces gyro drift estimation error by approximately 99% and attitude estimation error by about 72%–93%. The simulation results of typical on-orbit operation modes for microsatellites show that, although the attitude estimation accuracy decreases under more complex angular velocity conditions, the three-axis attitude error angles can still reach [ 1.31 1.37 7.94 ] T arcsec. The results further confirm that the proposed algorithm can effectively handle uncertain process and measurement noise, achieving high precision in both attitude and gyro drift estimation.

Under dynamic conditions, the smearing effect of star spots on the image plane reduces centroid extraction accuracy, which has an impact on attitude estima