基于神经网络的多源融合室内定位算法

针对WiFi信号在室内复杂环境下不稳定以及建筑物对地磁场的扭曲作用造成单一定位源定位精度不高的问题, 本文采用多源信息融合定位技术, 有效利用WiFi和地磁场的指纹数据来进行定位, 提出了一种改进的自适应差分进化算法来优化BP神经网络(improved differential evolution BP, IDEBP). 该方法通过改进差分进化算法的变异、交叉和选择操作来优化BP神经网络的权值和偏差, 有助于BP模型更好地学习WiFi和地磁场指纹数据的特征. 仿真结果表明, IDEBP算法能大大提高室内指纹定位的精度.

Multi-source Fusion Indoor Positioning Algorithm Based on Neural Network

WiFi signals are unstable in complex indoor environments and the distortion effects of buildings on the geomagnetic field results in the low accuracy of single-source positioning. Considering this, this study adopts multi-source information fusion positioning technology that can effectively use WiFi and fingerprint data of the geomagnetic field for positioning and proposes an improved adaptive differential evolution algorithm to optimize the BP neural network (IDEBP). This method optimizes the weights and deviations of the BP neural network by improving the mutation, crossover, and selection operation of the differential evolution algorithm, which helps the BP model to better learn the characteristics of WiFi and fingerprint data of the geomagnetic field. The simulation shows that the proposed algorithm greatly improves the accuracy of indoor fingerprint positioning.