【摘要】：隨著深空探測任務的增加,自主導航技術變得越來越重要。對于轉移軌道上的探測器而言,由于它離太陽與各個行星的距離比較遠,近地軌道上的自主導航方法無法滿足其導航要求,此時天文導航是非常有效的方法。天文導航是一種全自主的方法,具有精度高、誤差不隨時間而累積、抗干擾能力強以及可提供位置姿態(tài)等信息的優(yōu)點,非常適合于遠距離飛行、外界環(huán)境復雜多變、飛行時間長的導航任務。在火星探測器的轉移軌道上,地球與火星軌道之間有很多近地小行星,我們可以利用觀測小行星獲得信息來確定探測器的導航信息。在此,我們采用四體模型作為導航系統的狀態(tài)模型。由于采用小行星圖像信息和小行星星光矢量作為觀測量,會出現姿態(tài)估計誤差和敏感器指向誤差;而采用星光角距作為觀測量可以避免這兩方面對導航精度的影響,故觀測模型采用星光角距作為觀測量。由于導航系統的狀態(tài)方程和觀測方程不可避免地出現誤差,如果想獲得更精確的狀態(tài)估計值,就得用濾波估計方法系統的狀態(tài)量進行估計。由于深空探測環(huán)境復雜多變,導航系統的狀態(tài)模型噪聲時刻變化,與單一模型相比,多模型自適應估計方法通過一組并行濾波估計器進行估計,并且時刻計算模型概率,符合這種過程噪聲序列多變的需求,能達到自適應的效果。本文研究了多模型自適應估計與擴展卡爾曼濾波/無跡卡爾曼濾波相結合的方法,形成多模型自適應擴展卡爾曼濾波與多模型自適應無跡卡爾曼濾波這兩種方法,并將其用于基于小行星觀測信息的自主天文導航系統上。通過仿真實驗,將基于單一模型的天文導航系統與基于多模型的天文導航系統進行了詳細的比較,說明引入多模型自適應方法,可增強系統對環(huán)境的適應性,明顯提高天文導航系統的精度、連續(xù)性及可靠性。
[Abstract]:With the increase of deep space exploration missions, autonomous navigation technology becomes more and more important. For the probe in the transfer orbit, because of its distance from the sun and the planets, the autonomous navigation method in the low Earth orbit can not meet its navigation requirements. At this time, astronomical navigation is a very effective method. Astronomical navigation is a fully autonomous method, which has the advantages of high precision, no accumulation of errors with time, strong anti-interference ability and the ability to provide position and attitude information. It is very suitable for long distance flight and complex and changeable external environment. A long flight navigation mission. There are many near-Earth asteroids between the Earth and Mars orbit in the transfer orbit of the Mars probe. We can use the observation asteroids to obtain information to determine the navigation information of the spacecraft. Here, we use the four-body model as the state model of the navigation system. Because of the asteroid image information and asteroid starlight vector as observations, there will be attitude estimation error and sensor pointing error. The influence of these two aspects on the navigation accuracy can be avoided by using the starlight angle distance as the observation quantity, so the starlight angle distance is used as the observation quantity in the observation model. Due to the inevitable errors in the state equation and observation equation of navigation system, if we want to obtain more accurate state estimation, we have to estimate the state quantity of the system by using the filter estimation method. Because of the complex and changeable deep space exploration environment, the noise time of the state model of the navigation system changes. Compared with the single model, the multi-model adaptive estimation method is estimated by a set of parallel filter estimators, and the probability of the model is calculated at the same time. In accordance with the requirements of the process noise sequence, the adaptive effect can be achieved. In this paper, the method of combining multi-model adaptive estimation with extended Kalman filter / unscented Kalman filter is studied to form two methods: multi-model adaptive extended Kalman filter and multi-model adaptive unscented Kalman filter. It is used in autonomous astronomical navigation system based on asteroid observation information. Through the simulation experiment, the astronomical navigation system based on single model and the astronomical navigation system based on multi-model are compared in detail. It is shown that the adaptive method of multi-model can enhance the adaptability of the system to the environment. The accuracy, continuity and reliability of the astronomical navigation system are obviously improved.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN713

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