基于自适应UKF及位速测量辅助的大椭圆轨道卫星自主导航
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摘要
卫星在大椭圆轨道上运行，不仅其轨道动力学变化剧烈，而且要经历各种严酷的空间环境。因此实现大椭圆轨道卫星自主天文导航面临两个新的问题：1)由空间环境带来的不确定测量噪声导致导航精度降低；2)由大椭圆轨道动力学特点引起的近地点导航滤波精度波动。本文以实现大椭圆轨道卫星自主导航为目的，针对以上两大问题，进行了如下几个方面的研究：
研究了强跟踪UKF滤波算法。针对标准UKF算法对不确定测量噪声适应能力差的问题，将强跟踪滤波的思想推广到UKF滤波过程中，根据正交性原理，通过引入自适应因子，推导得到了强跟踪UKF算法。该方法不仅继承了强跟踪滤波对不确定测量噪声的自适应能力，而且不必计算和存储雅可比矩阵，可达到三阶的滤波精度。
针对UKF算法由于自适应计算及Unscented变换导致计算量增加的问题，研究进一步减小计算量，提高计算效率的滤波方法。当滤波过程中仅存在短时间不确定测量噪声时，滤波器只需在检测到不确定测量噪声存在时启动自适应运算。基于这一思想，研究了切换自适应UKF算法。通过大数定理及其推论，得到了切换自适应UKF算法的模式切换准则，在滤波中动态判断滤波器的工作模式，并进行相应的切换。Unscented变换通过Sigma采样点逼近非线性函数的概率密度函数，将状态扩展为2n+1维，增加了滤波的计算量。为了解决这一问题，进而研究了超球面变换采样方法，利用超球面变换代替Unscented变换，将采样点的个数缩减为n+2个，从而使计算量得到了极大的降低。与切换自适应UKF相比，基于超球面变换的自适应UKF算法能够绝对减少计算量。
研究了切换模糊自适应UKF滤波算法。通过在自适应算法中引入模糊推理，进一步调节自适应因子，提高导航滤波精度。为了使模糊自适应滤波器能够适应大范围不确定测量噪声的变化，对滤波器模糊推理系统的论域进行分析和划分，分别建立了正常情况和较大测量噪声下的模糊隶属度函数。在正常情况下的调节，主要是为了使滤波器能够较快的收敛；当测量噪声较大时，主要完成对不确定测量噪声的自适应。为了弥补模糊推理方法存在的学习能力较差，推理过程模糊性大等缺点，引入了BP神经网络处理方法，系统采用串联型结构，通过神经网络对模糊调节自适应因子进行后期处理。
研究了适合于大椭圆轨道卫星的自主导航方法。针对天文自主导航在近地点导航估计误差波动问题，引入位速测量辅助修正的方法。主要提出了两种具体方案：基于雷达高度计测高的方案和基于GPS测量修正的方案。第一种方案采用雷达高度计在近地点的高度测量，可以作为星光角距观测量的补充。分析了雷达高度计的适用范围及测量原理，建立了雷达高度计测量方程，设计了天文/雷达高度计自主导航方案。第二种方案在分析GPS对大椭圆轨道卫星覆盖范围的基础上，通过充分利用GPS伪距和伪距率的测量，在近地点得到卫星的位置、速度直接测量信息，建立了GPS测量方程并用于导航滤波计算中，通过与天文导航进行信息融合，最终得到高精度的导航输出。
最后，构建了大椭圆轨道卫星自主导航实验系统，将本文提出的强跟踪自适应UKF算法及切换模糊自适应UKF算法应用于大椭圆轨道卫星自主导航实验中，在不同水平的不确定测量噪声影响下进行了实验验证，实验结果表明，本文提出的自主导航方法是有效和可行的。
In highly elliptical orbits, the satellite orbit dynamics change severely and willexperience a variety of harsh space environment. Therefore, two new issues areraised to highly elliptical orbit satellite autonomous navigation system:1) The lownavigation precision caused by the uncertain measurement noise from the harshspace environment;2) The navigation estimate accuracy is fluctuate due to highlyelliptical orbit dynamic characteristics. To solve the problems, this paper carried outthe follow study in order to achieve highly elliptical orbit satellite autonomousnavigation.
Strong tracking adaptive UKF filter is studied. As the standard UKF algorithmis invalid to uncertain measurement noise, promote the idea of strong trackingadaptive filter to UKF filter. According to orthogonality principle, the strongtracking adaptive UKF is deduced through introducing adaptive factor. Thisalgorithm inherits the adaptive ability to uncertain noise of strong tracking adaptivefilter, omittes the calculation of Jacobin and achieves three order precision.
As the calculation of UKF is increased due to adaptive computing andUnscented Transformation, further methods to cut down calculation and improvecomputational efficiency are studied. If the uncertain measurement noise exists onlya short period of time, the filter need not perform adaptive calculation, but startadaptive calculation only when uncertain noise is detected. Based on this idea,switch adaptive UKF is studied. Through the switching law based on large numberstheorem, the filter mode is judged and switched. The Unscented Transformation isthe key point of UKF algorithm. The estimate precision is improved using Sigmapoint directly which approaches the probability density of nonlinear functioninstead of linearization. However, the dimension of system through UnscentedTransformation is2n+1which increased calculation. Therefore, the hypersphericaltransformation method instead of Unscented Transformation is studied. Thedimension of Sigma point is reduced to n+2using this method and the calculation iscut down significantly. Compared with switch adaptive UKF, this method reducedcalculation absolutely.
Switch fuzzy adaptive UKF is studied. The adaptive factor is further regulatedand the adaptive estimate precision is improved further more by fuzzy logic. Inorder to improve the adaptive ability of fuzzy adaptive UKF to overcome high leveluncertain noise, fuzzy membership functions are established in two modes: thenormal filter mode and the filter mode under high level noise. The purpose ofadaptive filter in normal mode is to get faster convergence and overcome theuncertain noise in the mode under high level noise. In order to deal with the disadvantages of learning ability and fuzzy inference, BP Neural Networks isintroduced to the filter process. The Neural Networks is connected with the fuzzysystem in series in order to improve the fuzzy adaptive factor.
Autonomous navigation methods suitable for highly elliptical orbit satellite arestudied. In order to overcome the filtering problem at perigee using celestialnavigation, the position/velocity measurements auxiliary correction method isintroduced. Two proposals are maily studied: autonomous navigation method basedon radar altimeter measurement and the auxiliary correction method using GPS. Inthe first proposal, the radar altimeter measurement is used as supplement tostar-earth angular observations. The radar altimeter application range andmeasurement principle is presented, the measurement equation is established, andthen the autonomous navigation method based on celestial&radar altimeter isproposed. In the second proposal, the coverage of GPS to highly elliptical orbitsatellite is analysised. According to the GPS position/velocity measurements, theGPS measurement equation is established and applied to navigation filter. Throughinformation fusion with celestial navigation, the high navigation output is achieved.
Finally, autonomous navigation experiment system is established, the strongtracking adaptive UKF algorithm and the switch fuzzy adaptive UKF algorithm areapplied to highly elliptical orbit satellite autonomous navigation experiment. Theexperiment is performed under different uncertain measurement noise levels. Theresults of the experiment show that the autonomous navigation methods put forwardby this thesis is effective and feasible.
研究了强跟踪UKF滤波算法。针对标准UKF算法对不确定测量噪声适应能力差的问题，将强跟踪滤波的思想推广到UKF滤波过程中，根据正交性原理，通过引入自适应因子，推导得到了强跟踪UKF算法。该方法不仅继承了强跟踪滤波对不确定测量噪声的自适应能力，而且不必计算和存储雅可比矩阵，可达到三阶的滤波精度。
针对UKF算法由于自适应计算及Unscented变换导致计算量增加的问题，研究进一步减小计算量，提高计算效率的滤波方法。当滤波过程中仅存在短时间不确定测量噪声时，滤波器只需在检测到不确定测量噪声存在时启动自适应运算。基于这一思想，研究了切换自适应UKF算法。通过大数定理及其推论，得到了切换自适应UKF算法的模式切换准则，在滤波中动态判断滤波器的工作模式，并进行相应的切换。Unscented变换通过Sigma采样点逼近非线性函数的概率密度函数，将状态扩展为2n+1维，增加了滤波的计算量。为了解决这一问题，进而研究了超球面变换采样方法，利用超球面变换代替Unscented变换，将采样点的个数缩减为n+2个，从而使计算量得到了极大的降低。与切换自适应UKF相比，基于超球面变换的自适应UKF算法能够绝对减少计算量。
研究了切换模糊自适应UKF滤波算法。通过在自适应算法中引入模糊推理，进一步调节自适应因子，提高导航滤波精度。为了使模糊自适应滤波器能够适应大范围不确定测量噪声的变化，对滤波器模糊推理系统的论域进行分析和划分，分别建立了正常情况和较大测量噪声下的模糊隶属度函数。在正常情况下的调节，主要是为了使滤波器能够较快的收敛；当测量噪声较大时，主要完成对不确定测量噪声的自适应。为了弥补模糊推理方法存在的学习能力较差，推理过程模糊性大等缺点，引入了BP神经网络处理方法，系统采用串联型结构，通过神经网络对模糊调节自适应因子进行后期处理。
研究了适合于大椭圆轨道卫星的自主导航方法。针对天文自主导航在近地点导航估计误差波动问题，引入位速测量辅助修正的方法。主要提出了两种具体方案：基于雷达高度计测高的方案和基于GPS测量修正的方案。第一种方案采用雷达高度计在近地点的高度测量，可以作为星光角距观测量的补充。分析了雷达高度计的适用范围及测量原理，建立了雷达高度计测量方程，设计了天文/雷达高度计自主导航方案。第二种方案在分析GPS对大椭圆轨道卫星覆盖范围的基础上，通过充分利用GPS伪距和伪距率的测量，在近地点得到卫星的位置、速度直接测量信息，建立了GPS测量方程并用于导航滤波计算中，通过与天文导航进行信息融合，最终得到高精度的导航输出。
最后，构建了大椭圆轨道卫星自主导航实验系统，将本文提出的强跟踪自适应UKF算法及切换模糊自适应UKF算法应用于大椭圆轨道卫星自主导航实验中，在不同水平的不确定测量噪声影响下进行了实验验证，实验结果表明，本文提出的自主导航方法是有效和可行的。
In highly elliptical orbits, the satellite orbit dynamics change severely and willexperience a variety of harsh space environment. Therefore, two new issues areraised to highly elliptical orbit satellite autonomous navigation system:1) The lownavigation precision caused by the uncertain measurement noise from the harshspace environment;2) The navigation estimate accuracy is fluctuate due to highlyelliptical orbit dynamic characteristics. To solve the problems, this paper carried outthe follow study in order to achieve highly elliptical orbit satellite autonomousnavigation.
Strong tracking adaptive UKF filter is studied. As the standard UKF algorithmis invalid to uncertain measurement noise, promote the idea of strong trackingadaptive filter to UKF filter. According to orthogonality principle, the strongtracking adaptive UKF is deduced through introducing adaptive factor. Thisalgorithm inherits the adaptive ability to uncertain noise of strong tracking adaptivefilter, omittes the calculation of Jacobin and achieves three order precision.
As the calculation of UKF is increased due to adaptive computing andUnscented Transformation, further methods to cut down calculation and improvecomputational efficiency are studied. If the uncertain measurement noise exists onlya short period of time, the filter need not perform adaptive calculation, but startadaptive calculation only when uncertain noise is detected. Based on this idea,switch adaptive UKF is studied. Through the switching law based on large numberstheorem, the filter mode is judged and switched. The Unscented Transformation isthe key point of UKF algorithm. The estimate precision is improved using Sigmapoint directly which approaches the probability density of nonlinear functioninstead of linearization. However, the dimension of system through UnscentedTransformation is2n+1which increased calculation. Therefore, the hypersphericaltransformation method instead of Unscented Transformation is studied. Thedimension of Sigma point is reduced to n+2using this method and the calculation iscut down significantly. Compared with switch adaptive UKF, this method reducedcalculation absolutely.
Switch fuzzy adaptive UKF is studied. The adaptive factor is further regulatedand the adaptive estimate precision is improved further more by fuzzy logic. Inorder to improve the adaptive ability of fuzzy adaptive UKF to overcome high leveluncertain noise, fuzzy membership functions are established in two modes: thenormal filter mode and the filter mode under high level noise. The purpose ofadaptive filter in normal mode is to get faster convergence and overcome theuncertain noise in the mode under high level noise. In order to deal with the disadvantages of learning ability and fuzzy inference, BP Neural Networks isintroduced to the filter process. The Neural Networks is connected with the fuzzysystem in series in order to improve the fuzzy adaptive factor.
Autonomous navigation methods suitable for highly elliptical orbit satellite arestudied. In order to overcome the filtering problem at perigee using celestialnavigation, the position/velocity measurements auxiliary correction method isintroduced. Two proposals are maily studied: autonomous navigation method basedon radar altimeter measurement and the auxiliary correction method using GPS. Inthe first proposal, the radar altimeter measurement is used as supplement tostar-earth angular observations. The radar altimeter application range andmeasurement principle is presented, the measurement equation is established, andthen the autonomous navigation method based on celestial&radar altimeter isproposed. In the second proposal, the coverage of GPS to highly elliptical orbitsatellite is analysised. According to the GPS position/velocity measurements, theGPS measurement equation is established and applied to navigation filter. Throughinformation fusion with celestial navigation, the high navigation output is achieved.
Finally, autonomous navigation experiment system is established, the strongtracking adaptive UKF algorithm and the switch fuzzy adaptive UKF algorithm areapplied to highly elliptical orbit satellite autonomous navigation experiment. Theexperiment is performed under different uncertain measurement noise levels. Theresults of the experiment show that the autonomous navigation methods put forwardby this thesis is effective and feasible.
引文
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