Low-cost sensors aided vehicular position prediction with partial least squares regression during GPS outage

Abstract

Vehicular position prediction is very important in intelligent transport systems (ITS), and the requirements of accuracy for position prediction have been significantly increasing in recent years. In this paper, we focus on designing a more low-cost and convenient method which can operate during GPS outages. In order to better deal with the position prediction during the lack of GPS signals, we introduce a windowed partial least squares regression (WPLSR) approach where vehicle position information from the low-cost sensors was used. Moreover, the window is adjustable and it reduces the step of regression in WPLSR algorithm. The sensor data outside the window that has nothing to do with the latest position prediction is eliminated. Therefore, the position accuracy can be improved significantly. Finally, the proposed method is evaluated by using road experiments from real urban areas. Compared with the conventional techniques such as PLSR and extended Kalman filter combined with an interactive multimodel (IMM-EKF), the results show that WPLSR presents the higher position accuracy especially during the GPS outages. (2016) 'Low-cost sensors aided vehicular position prediction with partial least squares regression during GPS outage', Int.

Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

“…Actual well trajectory and field test show that the bit position prediction model can effectively determine the bit position and determine its approaching trend under certain conditions, providing reference for anti-collision early warning. 1,2 Jaiswal and Jaidhar 3 studied the seismic propagation time measurements obtained in the borehole, including vertical seismic profiles, seismic measurements during drilling, and noise data generated by the bit. These travel time data are used to evaluate information parameters, including bit position, distance to drilling target, and velocity model parameters.…”