| 摘要: |
| 天文导航是一种广泛应用于深空探测任务中的节能、高效的导航方式。基于轨道动力学模型和星光角距的卡尔曼滤波方法已经被成功应用在天文导航系统中。在捕获段由于探测器所处动力学环境复杂，未建模的加速度误差，星历误差等都会造成过程噪声统计特性不完全。针对以上问题，提出一种根据新息和残差序列的变化趋势来调节过程噪声协方差阵的自适应平方根容积卡尔曼的方法（AQSCKF）。该方法先分别利用新息和残差计算调节因子，然后判断新息和残差的变化趋势，当新息和残差的变化趋势一致时，取二者调节因子的均值作为过程噪声方差阵的调节因子，对其进行调节。此外，本文还将该方法与传统的只利用新息或残差在线调节协方差阵的平方根容积卡尔曼滤波（SCKF）方法进行对比，仿真结果表明，在解决由于过程噪声统计特性不能完全已知的问题上，AQSCKF算法不仅能显著提高导航精度，并且具有很好的稳定性。 |
| 关键词: 自适应滤波 天文导航 过程噪声 卡尔曼滤波 |
| DOI：10.15982/j.issn.2095-7777.2016.03.007 |
| 分类号: |
| 基金项目:国家重点基础研究发展计划（2014CB744202） |
|
| An Adaptive Kalman Filter for Mars Spacecraft Acquisition Phase |
|
NING Xiaolin1, LI Zhuo1, HUANG Panpan2, YANG Yuqing1, LIU Gang1, FANG Jiancheng1
|
|
1.School of Instrumentation Science & Opto-electronics Engineering, BeiHang University(BUAA), Beijing 100191, China;2.School of Civil and Environmental Engineering, University of New South Wales, Sydney 2052, Australia
|
| Abstract: |
| Celestial navigation is an energy saving and efficient way of autonomous navigation for deep space probes. Kalman filter has been successfully applied in the Celestial navigation system. During the acquisition phase, due to the complex dynamic environment, unmolded acceleration error and the ephemeris error etc. may cause the statistics of process noise uncertainty. To overcome the problem, a method named AQSCKF based on the trend of the innovation sequences and residual sequences to scale the process noise covariance matrix is proposed in this paper. In the first place, it calculates the scale factor based on the innovation and residual separately. Then, by comparing the trend of the two factors, the scale factor of the new method is set as the average. In addition, the navigation performance of traditional SCKF, the method only using innovation or residual to scale Q and AQSCKF is also compared by simulation. The simulation results show that the new method yields better performance than the traditional methods in solving the problem caused by the uncertainty of the process noise, furthermore it also shows a good stability. |
| Key words: adaptive filter celestial navigation process noise Kalman filter |
