深空探测器接近段自主导航的滑动窗口自适应滤波方法

doi:10.16183/j.cnki.jsjtu.2022.233

摘要/Abstract

摘要：

当深空探测器接近目标行星时,由于目标行星引力快速增加,轨道动力学模型会出现较快的加速度变化.由于噪声协方差不完全已知,所以传统的滤波方法无法获得导航参数的最优估计,难以满足接近段导航系统的性能要求.为满足系统高稳定性和高精度需求,提出一种基于系统噪声协方差的滑动窗口自适应非线性滤波方法.通过构造系统噪声协方差更新函数,使用滑动窗口对噪声协方差平稳化处理,将速度噪声引起的误差与位置噪声引起的误差隔离开,实时更新所使用的滤波参数信息,自适应调节系统噪声协方差.以火星探测器为例进行仿真,仿真结果表明,相对于传统的无迹卡尔曼滤波方法,该方法获取的位置精度和速度精度分别提高90.97%和66.17%,抑制了系统模型上快速变化的积分误差,并解决传统滤波方法的发散问题.此外,分析了滤波周期和窗口大小对导航性能的影响,为深空探测自主导航提供了一种可行的自适应滤波新方法.

关键词: 自适应滤波, 卡尔曼滤波, 自主导航, 深空探测

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

中图分类号:

V448.22

张文佳, 马辛. 深空探测器接近段自主导航的滑动窗口自适应滤波方法[J]. 上海交通大学学报, 2022, 56(11): 1461-1469.

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.

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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