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UWB Location Algorithm Based on BP Neural Network

机译:基于BP神经网络的UWB位置算法

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摘要

In order to solve the problem that in the traditional trilateral positioning algorithm, the final positioning error is large when there is a certain error in the measured three-sided distance, a UWB positioning algorithm based on Back Propagation (BP) neural network is proposed. The algorithm utilizes the fast learning characteristic and the ability of approximating any non-linear mapping of neural network, and realizes the location of the mobile label through the TOA measurement value provided by the base station and the BP neural network. By comparing the traditional trilateral positioning algorithm, the BP neural network algorithm based on four distance inputs and the BP neural network algorithm based on four distance inputs with trilateral positioning coordinates, it can be seen that the positioning error of traditional trilateral positioning algorithm is 30 cm, and the positioning error of the positioning algorithm based on the BP neural network proposed in this paper is 10 cm. The positioning algorithm proposed in this paper can effectively reduce the impact of distance measurement error and non-line-of-sight propagation during wireless signal transmission, and obviously improve the positioning accuracy of UWB positioning. The UWB positioning algorithm based on BP neural network proposed in this paper has been used to locate the vehicle in the process of automatic parking and has better real-time and accuracy.
机译:为了解决在传统的三边定位算法中的问题中,当测量的三边距离存在一定的误差时,最终定位误差大,提出了一种基于反向传播(BP)神经网络的UWB定位算法。该算法利用快速学习特性和近似神经网络的任何非线性映射的能力,并通过基站和BP神经网络提供的TOA测量值来实现移动标签的位置。通过比较传统的三边定位算法,基于四个距离输入的BP神经网络算法和三边定位坐标的四个距离输入的BP神经网络算法,可以看出传统三边定位算法的定位误差为30厘米并且基于本文提出的基于BP神经网络的定位算法的定位误差为10厘米。本文提出的定位算法可以有效地减少无线信号传输期间距离测量误差和非视线传播的影响,并显然提高了UWB定位的定位精度。基于本文提出的基于BP神经网络的UWB定位算法已被用于在自动停车过程中定位车辆,并且具有更好的实时和准确性。

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