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Hyperbolic-weighted centroid Indoor Location Algorithm based on Kalman Filter

机译:基于卡尔曼滤波的双曲加权质心室内定位算法

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The ultra-wideband(UWB) signal is affected by systematic measurement error and Not-Line-of-Sight(NLOS) error when it propagates in complex indoor environment, which leads to poor positioning accuracy and stability. In order to reduce the influence of error on the accuracy of positioning results, a Hyperbolic-weighted centroid algorithm based on Kalman Filter is proposed in this paper. The method is based on the time difference of arrival(TDOA) location model. The measured value of the TDOA distance difference is first filtered by Kalman Filter, and then the obtained results are used as the input of the Hyperbolic location algorithm to acquire the initial solution. For further offsetting the effect of error on positioning accuracy, a weighted centroid technique is applied. We evaluate the performance of the proposed method in both simulation and experimental results. The experimental results show that the position accuracy is effectively improved by the algorithm. When the measurement error satisfies the Gaussian distribution and the standard deviation is 0.1 m, the Root Mean Square Error(RMSE) of the location results is about 11 cm.
机译:超宽带(UWB)信号在复杂的室内环境中传播时,会受到系统性测量误差和视线之外(NLOS)误差的影响,从而导致定位精度和稳定性较差。为了减少误差对定位结果精度的影响,提出了一种基于卡尔曼滤波的双曲线加权质心算法。该方法基于到达时间差(TDOA)位置模型。 TDOA距离差的测量值首先通过卡尔曼滤波器进行滤波,然后将获得的结果用作双曲线定位算法的输入以获取初始解。为了进一步抵消误差对定位精度的影响,应用了加权质心技术。我们在仿真和实验结果中评估了该方法的性能。实验结果表明,该算法有效提高了定位精度。当测量误差满足高斯分布且标准偏差为0.1 m时,定位结果的均方根误差(RMSE)约为11 cm。

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