Vehicle Tracking based on Kalman Filter Algorithm

Received signal strength indicator (RSSI) is a difficult technique to accurately estimate the distance between two participating entities because of the obscure environmental factors that distort the signal’s strength. In this study, we demonstrate that RSSI can be used in combination with the Kalman Filter to identify the position of a node in a wireless vehicular network. By observing a series of measurements and utilizing a model of the node’s trajectory, we can filter the noisy RSSI measurements to obtain a more accurate estimation of the node’s position. In our experiment, we gathered RSSI measurements from a mobile node, and used this data in combination with the Kalman Filter to estimate the position of the mobile node within 10 feet of the true position. Throughout this report, we demonstrate our implementation of the Kalman Filter, which is conceptually two Kalman Filters condensed into a single filter. Furthermore, we present the results of our experiments that display accuracy as close as 4 ft. from the true position.