Taylor-series expansion location algorithm based on RBF neural network

In Line-Of-Sigh(tLOS) environment,Taylor-series expansion location algorithm has goodish location accuracy.But in Non-Line-Of-Sigh(tNLOS) environment,location accuracy of Taylor’s algorithm degrades greatly.In order to mitigate the effect of NLOS propagation,a Taylor-series expansion location algorithm based on the RBF neural network is proposed.The fast study and non-linear approach capacity of the neural network is made use of to correct the error of NLOS propagation,then position is calculated by Taylor’s algorithm.The simulation results indicate that the effect of NLOS propagation is mitigated by Taylor’s algorithm based on the RBF neural network.Its location accuracy is significantly improved and the performance of this algorithm is better than that of Taylor’s algorithm,Chan’s algorithm and LS’s algorithm in NLOS environment.