基于BP神经网络的ZigBee无线定位边界效应优化

Boundary effects optimization of ZigBee wireless location based on BP neural network

针对基于ZigBee技术的井下定位区域内定位误差在边界处定位节点会从网络外部向网络内部发生一定偏移,致使定位误差相对较大,但正负方向固定不变的分布规律,定义了定位边界效应,并提出了针对边界效应的优化方法。利用最小二乘法对边界区域的平均误差做二次函数拟合,并通过改进的BP神经网络预测针对不同节点间距对应的网络模型的边界区域补偿函数。实验结果表明,由BP神经网络预测的补偿函数可大幅改善边界区域的定位精度,使边界区域定位误差达到1m以下。

By analyzing distribution law of localization error in underground wireless network localization area based on ZigBee technology,namely localization nodes will offset from outside to the inside of the network at the boundary,so the localization error is great,but the positive and negative directions are fixed,the boundary effect was defined and a optimization method for the boundary effects was put forward.The least-squares method was used to fit quadratic functions of average error in the boundary area,improved BP neural network was used to predict compensation function of the boundary area for network model corresponding to different node spacing.The experimental results show that thecompensation function of BP neural network can significantly improve localization accuracy of boundary area,and the localization error of boundary area is one meter or less.