【摘要】：利用衛(wèi)星導航系統(tǒng)和捷聯(lián)慣性導航系統(tǒng)多傳感器進行組合導航達到高精度導航要求在民用領域和軍用領域是國內外研究的熱點。與此同時,信息融合方法的突飛猛進為提升組合導航的精度提供了重要支撐。本文基于北斗衛(wèi)星導航系統(tǒng)和捷聯(lián)慣性導航系統(tǒng)組合導航的緊組合方式開展信息融合方法研究。信息融合方法主要將北斗衛(wèi)星導航系統(tǒng)的偽距和偽距率與捷聯(lián)慣性導航系統(tǒng)的導航數(shù)據(jù)進行時空配準,數(shù)據(jù)關聯(lián)和數(shù)據(jù)融合。本文在數(shù)據(jù)關聯(lián)方面,提出了一種自適應概率數(shù)據(jù)關聯(lián)法。自適應概率數(shù)據(jù)關聯(lián)法提出適應導航組合新息的自適應加權的修正參數(shù),并通過仿真實驗分析可得出自適應概率數(shù)據(jù)關聯(lián)法的數(shù)據(jù)關聯(lián)效果要優(yōu)于其他關聯(lián)算法。自適應概率數(shù)據(jù)關聯(lián)法選擇不同的參數(shù),其數(shù)據(jù)關聯(lián)效果也有所不同,因此在實際應用中要根據(jù)實際情況選擇合適的參數(shù)來達到最佳的數(shù)據(jù)關聯(lián)效果。本文在數(shù)據(jù)融合方面,在粒子群優(yōu)化粒子濾波的基礎上運用一種修正權重粒子群優(yōu)化粒子濾波算法通過加入優(yōu)勢速度和劣勢速度來優(yōu)化粒子的更新模式,達到修正改變粒子的權重,來綜合粒子群優(yōu)化粒子濾波算法的全局和局部的搜索能力,讓粒子收斂加快,減小粒子濾波中陷入局部最優(yōu)的概率,可以在較短的時間內實現(xiàn)全局精確定位。本文最后對北斗衛(wèi)星導航系統(tǒng)和捷聯(lián)慣性導航系統(tǒng)組合導航的緊組合方式信息融合進行仿真實驗對比,實驗表明經過數(shù)據(jù)關聯(lián)和數(shù)據(jù)融合兩個重要步驟的優(yōu)化,改進算法的性能表現(xiàn)無論是在空間位置上,還是空間速度上的誤差估計和精度上都表現(xiàn)出明顯的優(yōu)勢。
[Abstract]:Multi-sensor integrated navigation using satellite navigation system and strapdown inertial navigation system to achieve high accuracy navigation requirements in civil and military fields is a research hotspot at home and abroad. At the same time, the rapid development of information fusion method provides an important support for improving the accuracy of integrated navigation. In this paper, the information fusion method is studied based on the tight combination of Beidou Satellite Navigation system and Strapdown Inertial Navigation system. The information fusion method mainly uses the pseudo-range and pseudo-range rate of Beidou satellite navigation system and the navigation data of strapdown inertial navigation system for space-time registration, data association and data fusion. In this paper, an adaptive probabilistic data association method is proposed for data association. Adaptive probabilistic data association method proposed adaptive weighted parameters for navigation integrated innovation, and through simulation analysis, it can be concluded that the data association effect of adaptive probabilistic data association method is better than that of other association algorithms. The adaptive probabilistic data association method selects different parameters, and the data association effect is different. Therefore, the optimal data association effect should be achieved by selecting the appropriate parameters according to the actual situation in the practical application. In this paper, in the aspect of data fusion, a modified weighted particle swarm optimization particle filter algorithm is used to optimize the particle updating mode by adding the superior speed and inferior speed on the basis of particle swarm optimization particle filter. By modifying and changing particle weight to synthesize the global and local searching ability of particle swarm optimization particle filter algorithm, the convergence of particles can be accelerated and the probability of falling into local optimum in particle filter can be reduced. Global positioning can be achieved in a short time. In the end of this paper, the information fusion of the tight integrated mode of Beidou satellite navigation system and strapdown inertial navigation system is simulated and compared. The experiment shows that the data association and data fusion are optimized by two important steps: data association and data fusion. The performance of the improved algorithm shows obvious advantages both in the spatial position, in the error estimation and accuracy of the spatial velocity.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN967.2

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