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首页> 外文会议>IEEE International symposium on Personal, Indoor, and Mobile Radio Communications >A ToA/IMU indoor positioning system by extended Kalman filter, particle filter and MAP algorithms
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A ToA/IMU indoor positioning system by extended Kalman filter, particle filter and MAP algorithms

机译：基于扩展卡尔曼滤波，粒子滤波和MAP算法的ToA / IMU室内定位系统

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摘要

This work introduces an indoor positioning system (IPS) which is a combination of wireless sensor network (WSN) and inertial navigation system (INS) for locating a moving object indoor. Here, WSN is adopted to measure the ranges from the unknown node to those anchor nodes by time of arrival (ToA) method. The core of the INS is the inertial measurement unit (IMU), which consists of accelerometers and gyroscopes. The real time inertial measurements from IMU and the range information by ToA method are both transmitted to processing terminal, where we propose to use three kinds of recursive Bayesian algorithms to make use of data to obtain the location estimations. The experimental results show that even with only two anchor nodes, the estimation accuracy of hybrid method by these three algorithms is higher than both standalone ToA method with 3 anchors and pure inertial solution.
机译：这项工作介绍了一种室内定位系统（IPS），该系统是无线传感器网络（WSN）和惯性导航系统（INS）的组合，用于在室内定位移动物体。在这里，采用WSN通过到达时间（ToA）方法测量从未知节点到那些锚点的范围。 INS的核心是惯性测量单元（IMU），它由加速度计和陀螺仪组成。来自IMU的实时惯性测量和ToA方法的距离信息都传输到处理终端，在此我们建议使用三种递归贝叶斯算法来利用数据来获得位置估计。实验结果表明，即使只有两个锚节点，这三种算法的混合方法的估计精度也比具有三个锚和纯惯性解的独立ToA方法都高。

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