Accurate Localization using Kalman Filter Based Multisensor Fusion of GPS and Accelerometer Data

The Global Positioning System (GPS) is a popular and widely used tool for finding location outdoors. However, it has some limitations, especially in areas where signals can bounce off buildings or objects, causing errors. This paper introduces a solution using sensor fusion—a technique that combines information from different sensors. In this case, it uses both GPS and motion data from a smartphone’s accelerometer. These two sources are combined using a Kalman filter, a smart algorithm that helps improve accuracy. As a result, the system provides more reliable and precise navigation. GPS error is determined by the horizontal accuracy. The horizontal accuracy of collected GPS data ranges from 21.5 meters to 3.94 meters, which varies according to the surroundings, obstacles, etc. After the fusion, the error is reduced to the range of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> degrees for both latitude and longitude, equal to 1.11 meters. A chi-square test is a statistical technique used for comparing the actual result and the prediction. The test was conducted to ensure that 99.95% of the measured data points lie within a certain range. The boundary of a certain region is determined by the vertical and horizontal accuracy of the GPS.