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Indoor location tracking using AGPS and Kalman filter

机译:使用AGPS和卡尔曼滤波器进行室内位置跟踪

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In this paper, the proposed system depends on the assist GPS (AGPS) idea in order to calculate the user position inside a building by using a roof-antenna (at known position) that has LOS with satellites. The antenna receives navigation data and sends it to the user using a wireless network. The distance between the user and the roof antenna is assumed less than 300m. In this case, the time delay between them is approximately equal to 1 Clear/Acquisition (C/A) code chip period, and it is detected by using autocorrelation with error in cm. In the simulations, real data from a GPS receiver were used to track user moves inside building through a path with constant velocity. The performance of system under AWGN and effect of bias due to multipath and NLOS is improved using Extended Kalman filter (EKF). The results with EKF are compared with Nonlinear Least Square method (Nonlinear LS) at different processing time and noise level. EKF gives high improvement compared with Nonlinear LS. The overall system with EKF is simple to implement and generally, the error in user position small and acceptable for most indoor applications.
机译:在本文中,提出的系统依赖于辅助GPS(AGPS)思想,以便通过使用带有卫星视距的屋顶天线(在已知位置)来计算建筑物内的用户位置。天线接收导航数据,并使用无线网络将其发送给用户。假设用户与屋顶天线之间的距离小于300m。在这种情况下,它们之间的时间延迟大约等于1个清除/获取(C / A)代码码片周期,并且通过使用具有cm误差的自相关进行检测。在仿真中,来自GPS接收器的真实数据用于跟踪用户通过恒定速度的路径在建筑物内移动。使用扩展卡尔曼滤波器(EKF)可以改善AWGN下的系统性能以及由于多径和NLOS引起的偏置效应。将EKF的结果与非线性最小二乘法(Nonlinear LS)在不同的处理时间和噪声水平下进行比较。与非线性LS相比,EKF有了很大的改进。带有EKF的整个系统易于实现,通常,用户位置的误差很小,对于大多数室内应用而言是可以接受的。

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