DHOHF-Elman neural network algorithm for indoor localization

Traditional Elman neural network algorithm has relatively low positioning accuracy under indoor environment, which is a problem for indoor positioning system. To enhance the accuracy,an improved DHOHF-Elman (Elman neural network with Double Hidden layers and Output-Hidden Feedback, DHOHF-Elman) algorithm based on UWB (Ultra Wideband) is proposed. Through improving the nerve nettopology structure by adding the second hidden layer and the second receiving layer, the algorithm improves the performance of the neural network,result in achieving the effect of double hidden layer feedback.Then, large amount of data simulated though channel model is used to train and test the constructed neural network model, indicatingthattheimproved neural network algorithm has higher positioning accuracy and better convergence speed than traditional neural network algorithm.Finally, the simulation results verify the superiority and effectiveness of the new algorithm in different environments under the condition of consisting with and without WGN (White Gaussian Noise) respectively.