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An improved attitude information fusion algorithm based on particle filtering

机译：一种改进的基于粒子滤波的姿态信息融合算法

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In view of the noise and measurement errors of sensors, the data in attitude information measurement system should be filtered. Based on the previous algorithm Kalman filtering, this paper proposes a more effective algorithm using particle filtering to solve the problem of accuracy appearing in Kalman filtering. Using Bayes theory, the estimate of the state of a system is accomplished by computation of probability distribution. The data of the sensors is filtered by a prior estimate with the characteristic of the system and a posterior estimate based on the data. This process is implemented recursively and achieves a real-time estimate of the state. The algorithm proposed in this paper tries to approximate the posterior probability density by random discrete measure. It generates two sets particles each time to fuse the data of two sensors which makes the fusion more accurately. The algorithm is verified by Matlab using the data gathering from some motional vehicles and the results show the feasibility and good performance of the algorithm.
机译：考虑到传感器的噪声和测量误差，应对姿态信息测量系统中的数据进行过滤。在原有算法卡尔曼滤波的基础上，提出了一种更有效的粒子滤波算法，以解决卡尔曼滤波出现精度问题。使用贝叶斯理论，通过概率分布的计算可以完成对系统状态的估计。传感器的数据通过具有系统特征的先验估计和基于数据的后验估计进行过滤。此过程是递归实现的，并且可以实时估计状态。本文提出的算法试图通过随机离散测度来近似后验概率密度。每次生成两组粒子以融合两个传感器的数据，从而使融合更加准确。 Matlab利用从某些运动车辆收集的数据对该算法进行了验证，结果表明了该算法的可行性和良好的性能。

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