A theoretical investigation on moving average filtering solution for fixed-path map matching of noisy position data

Abstract

Precisely estimation of moving object locations from position sensors promises useful implications for many fields of engineering. The mapping of a moving object on a predefined path is an important process for object tracking and remote control applications. Owing to measurement noises of sensors and uncertainties, the measured object location may not precisely match to paths or roads in a map. This study presents a numerical method for a low computational-complexity solution of point to arc type mapping problems. This method has two main tasks: a noise reduction task by short-time moving average filtering of noisy two-dimensional position data, and a map matching task to estimate exact position of an object on a map. To evaluate performance of the investigated method, the algorithm is applied for bus route tracking simulations and results are discussed for several road scenarios and various levels of noise.