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Multi-sensor fusion method using kalman filter to improve localization accuracy based on android smart phone

机译:基于Android智能手机的多传感器融合方法提高了基于Android智能手机的本地化精度

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Android smart phone can be used in ITS (Intelligent Transportation Systems) to obtain people and vehicle's location since it is integrated with GPS, direction sensor and acceleration sensor. Because the GPS built in smart phone always has an error of dozens of meters, improving the positioning accuracy is necessary before introducing it into ITS. This paper proposed an approach to improve the accuracy to the street level and get a smooth trajectory without jump points. It is convenient for everyone to use it because almost everyone has a smart phone. Firstly, road-matching algorithm is used to improve the localization accuracy to street level; secondly, speed and direction information are introduced to better reflect the real trajectory; thirdly, Kalman filter is used to eliminate the jump points and make the trajectory smooth; finally, the optimal result obtained from the process of Kalman filter is interpolated to reflect more details. The experiment result shows that the approach is effective.
机译:Android智能手机可用于其(智能运输系统),以获取人员和车辆的位置,因为它与GPS,方向传感器和加速度传感器集成。由于智能手机内置的GPS始终存在几十米的误差,因此在将其引入其之前,需要提高定位精度。本文提出了一种提高街道水平的准确性的方法,并在没有跳跃点的情况下获得平滑轨迹。大家都可以方便地使用它,因为几乎每个人都有智能手机。首先,道路匹配算法用于提高街道水平的本地化精度;其次,引入了速度和方向信息以更好地反映真实的轨迹;第三,卡尔曼滤波器用于消除跳跃点并使轨迹平滑;最后,从卡尔曼滤波器的过程中获得的最佳结果被插值以反映更多细节。实验结果表明该方法是有效的。

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