LSTM-Based High Precision Pedestrian Positioning

Prevention of pedestrians traffic accidents has become an important issue in intelligent transportation system. Pedestrian to vehicle communication, in which pedestrians send their position information to surrounding vehicles, is effective in reducing accidents when pedestrians are out of sight of vehicles. However, in urban canyons, the precision of pedestrian positioning via GPS may be significantly degraded due to obstructions and reflections of roadside buildings. To solve this problem, our previous work has suggested using vehicles as anchors for pedestrian positioning, and estimating pedestrian-vehicle distance from instantaneous channel state information (CSI), by using Support Vector Regression (SVR). In this paper, based on the fact that pedestrian-vehicle distance changes continuously, we propose to estimate the distance from a CSI sequence by using an LSTM (Long short-term memory) model, and solve the problem of packet loss that may affect the CSI collection. Simulations using 3D ray tracing show that the proposed method reduces the distance error and the horizontal positioning error by 24.8 % and 41.7%, respectively, compared to the previous method using SVR.