A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe

doi:10.16183/j.cnki.jsjtu.2022.233

Abstract

Abstract:

When the deep space probe approaches the target planet, due to the rapid increase of the gravity of the target planet, the orbital dynamics model will have a rapid acceleration change. Because the noise covariance is not completely known, the traditional filtering algorithm cannot obtain the optimal estimation of navigation parameters, which is difficult to meet the performance requirements of the approach navigation system. In order to meet the requirements of high stability and accuracy of the system, a sliding window adaptive nonlinear filtering algorithm based on system noise covariance is proposed. By constructing the system noise covariance update function and using the sliding window to smooth the noise covariance, the errors caused by velocity noise and position noise are separated, the filter parameter information used is updated in real time, and the system noise covariance is adjusted adaptively. Taking the Mars probe as an example, the simulation results show that, compared with the traditional unscented Kalman filtering method, the position accuracy and velocity accuracy of the proposed method are improved by 90.97% and 66.17% respectively, which suppresses the rapidly changing integral error on the system model, and solves the divergence problem of the traditional filtering method. In addition, the influence of filtering period and window size on navigation performance is analyzed, which provides a feasible new adaptive filtering method for autonomous navigation of deep space exploration.

Key words: adaptive filtering, Kalman filter, autonomous navigation, deep space exploration

CLC Number:

V448.22

ZHANG Wenjia, MA Xin. A Sliding Window Adaptive Filtering Algorithm for Autonomous Navigation of the Approach Phase of Deep Space Probe[J]. Journal of Shanghai Jiao Tong University, 2022, 56(11): 1461-1469.

Figures/Tables 11

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参数	数值
a'/km	1.932×10⁸
e	0.2364
i'/(°)	23.455
升交点赤经/(°)	0.258
近地点角距/(°)	71.347
真近角点/(°)	85.152
接近火星时间	1997-01-04T17:03:13.000

参数	数值
f'/mm	2 013.4
FOV/mrad	10×10
R/(rad·像素^-1)	10
CCD平面大小/像素	1024×1024
像素大小/m	21

滤波方法	估计误差平均值		估计误差最大值
滤波方法	位置/km	速度/ (m·s^-1)	位置/km	速度/ (m·s^-1)
UKF	92.221 2	0.879 7	355.462 5	11.166 9
AQUKF	8.323 4	0.297 6	146.019 5	11.173 0

T/s	估计误差平均值		估计误差最大值
T/s	位置/km	速度/(m·s^-1)	位置/km	速度/(m·s^-1)
120	3.106 5	0.158 1	51.003	11.904 3
300	4.103 0	0.167 8	78.635	12.492 3
600	8.323 4	0.297 6	146.019	11.173 0
900	7.797 1	0.280 2	102.540	9.872 7
1 200	11.574 0	0.538 7	109.420	56.290 3

N	估计误差平均值		估计误差最大值
N	位置/km	速度/(m·s^-1)	位置/km	速度/(m·s^-1)
1	8.572 8	0.316 1	150.67	11.197 3
5	8.412 2	0.300 1	148.27	11.178 1
10	8.323 4	0.297 6	146.02	11.173 0
50	8.545 2	0.312 8	141.62	11.168 2
100	8.850 2	0.312 7	140.63	11.167 5
300	9.579 0	0.307 9	139.87	11.167 2