An Adaptive Kalman Filter for Mars Spacecraft Approach Phase

Abstract

Celestial navigation technology is a kind of navigation means which is suitable for deep space exploration. It has been widely used in deep space exploration field. In the practical operation of deep space detector, Kalman filter is usually used as the optimal estimation method due to the existence of process noise and measurement noise. When deep space probe is in the approach section of the orbit, the acceleration of the probe changes sharply, which leads to the increase of the uncertainty of the navigation system process noise, so the process noise covariance cannot be accurately known. To solve these problems, adaptive Q cubature Kalman filter (AQCKF) based on system noise covariance adjustment was proposed in this paper. In this method, the estimated covariance of process noise at the last moment and the observed covariance of process noise at the present moment were considered comprehensively. The method used the weighted factor to adjust noise covariance online, which made the filtering method more optimized. At the same time, in this paper was taken the Mars probe as an example to be simulated. Simulation results show that compared with Cubature Kalman Filter (CKF), the average position error of AQCKF method was 10.2359 km, and the average velocity error was 0.3224 m/s.This method can not only solve the problem of error divergence, but also improve the stability of navigation system. In addition, the paper also analyzes the influence of weighted factor on navigation performance, which can effectively solve the problem of navigation accuracy reduction when deep space probe is in the approach segment.

Keywords

deep space exploration / autonomous navigation / adaptive filtering / Kalman filter

References

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