基于Elman神经网络的室内定位算法

Indoor positioning algorithm based on Elman neural network

针对传统室内定位算法精度较低的问题,提出一种基于Elman神经网络的室内定位算法。使用Elman神经网络进行指纹库插值扩充,完备指纹库,两者结合减少定位误差,并对该算法模型进行了实验验证。实验结果表明:在0.8 m×0.8 m×0.8 m环境中,所提算法的平均定位误差为4.6 cm,满足室内定位对于精度的要求。

Aiming at the low accuracy of traditional indoor positioning algorithm,an indoor positioning algorithm based on Elman neural network was proposed.The Elman neural network is used to expand the fingerprint database interpolation,complete the fingerprint database,and reduce the positioning error,and the algorithm model is verified by experiments.The experimental results show that the average positioning error of the proposed algorithm is 4.6 cm in 0.8 m×0.8 m×0.8 m environment,which meets the accuracy requirements of indoor positioning.