关键词: 气动捕获, 能观性分析, 参数不确定, 施密特卡尔曼滤波

Abstract: The observability analysis and navigation filtering algorithms during Mars aerocapture are studied. The observability of the system is analyzed using a method based on Lie derivatives, with the effects of atmospheric density uncertainty on the system's observability degree being considered. The unbiasedness of the Schmidt Kalman Filter under the condition of time varying parameters is proven, in response to the system model nonlinearity and atmospheric density uncertainty. The Extended Schmidt Kalman Filter (ESKF) algorithm is introduced, which effectively improves the accuracy of state estimation during aerocapture. Through simulation verification, the ESKF algorithm shows better estimation performance compared to traditional methods, providing effective theoretical and methodological support for the execution of Mars aerocapture missions.

Key words: aerocapture, observability analysis, parameter uncertainty, Schmidt Kalman filter