Atmospheric Density Modeling and Updating for Autonomous Navigation During Mars Aerocapture

The uncertainty of atmospheric density during Mars aerocapture is investigated. The atmospheric density of Mars is modeled as a Gaussian Random Field, and it is truncated using Karhunen-Loève Expansion. To enable real-time updating of the atmospheric density model, a low-Rank Kalman Filter algorithm is introduced, which directly updates the eigenvectors and eigenvalues of the Mars atmospheric density covariance matrix, avoiding the issue of high dimensionality. Finally, the updated atmospheric density is incorporated into the Unscented Kalman Filter for spacecraft navigation. Simulation results demonstrated the effectiveness of the proposed algorithm.