Dead Reckoning in Emergency Vehicle Preemption System Using Deep Output Kernel Learning and Extended Kalman Filter

Abstract

Emergency Vehicle Preemption (EVP) system plays an important role in reducing the response time of emergency vehicles by around 15–50%. Emergency Vehicle location estimation during Global Positioning System (GPS) outages is a challenging task in EVP. To address this issue, a location estimation scheme has been proposed by applying deep output kernel learning and extended Kalman filter using Inertial Measurement Unit (IMU) dead reckoning. The proposed scheme reduces the error in the estimation of velocity, attitude, and position by dynamically adapting the noise parameters of the extended Kalman filter. This system provides navigation solutions for emergency vehicles. Initially, four test routes were selected and the Position, velocity and attitude (pitch and roll angle) were measured and applied to the proposed scheme. Training and testing were conducted for the measured datasets. The performance of the proposed scheme was measured using five statistical methods during training and testing, and a comparison was made with other existing methods. The simulation results show that the proposed scheme performed well, in the four test routes. Finally, two different case studies were conducted using the proposed scheme and the performance was compared with three other methods. According to the results, the proposed scheme showed improvements in detection accuracy compared to the existing methods during GPS outages of 71.94% and 62.83% for Trajectory-1 and Trajectory-2, respectively.