A novel method is proposed for reducing the errors in distance measuring sensors namely Radar to accurately detect the relative distance of any Autonomous vehicle in a surface movement scenario. Sensor distance outputs are proposed to be taken with appropriate signal conditioning as input to the well known Kalman filter and an appropriate program is proposed to be written in Mathematica-11 on a Broad-Com2837 based Linux Operating system. The distance sensor namely radar data is simulated using Mathematica-11 real random variable built in function. This data is applied as input to the scalar Kalman filter and error corrected data is obtained at the output. The measured values and error corrected values and true values of the radar are plotted along with error reduction scenario. It is observed that the considerable error reduction was obtained through this method.
Varun, C., Rama Krishna, K., & Subba Rao, S. P. V. (2019). Error correction of radar long distance data using kalman filter for autonomous vehicle movement. International Journal of Recent Technology and Engineering, 8(1), 2555–2558.