Accurate Indoor Positioning Prediction Using the LSTM and Grey Model

The indoor positioning prediction technologies are developed to locate and predict actual positions of the objective indoors, and can be applied to smart elderly-caring application scenarios, helping to discover and reveal irregular life routines or abnormal behavior patterns of the elderly living at home alone. In this paper, we focus on accurate indoor positioning prediction and introduce an improved prediction model for IoT sensing data based on the LSTM and Grey model. In order to enhance the prediction ability of nonlinear samples in IoT sensing data and improve the prediction accuracy of the model, we propose to incorporate into and utilize the advantages of the LSTM model in dealing with nonlinear time series data of different spans, and the ability of the Grey model in dealing with incomplete information and in eliminating residual errors generated by LSTM. To demonstrate the effectiveness and performance gains of the model, we setup experiments based on the indoor trajectory dataset. Experimental results show that the model proposed in this paper outperforms its competitors, producing an arresting increase of the positioning prediction accuracy, with the RSME for the next day and the next week being 63.39% and 54.86%, respectively, much lower than that of the conventional models.