Improving the accuracy of wireless lan based location determination systems using kalman filter and multiple observers

Various RF based location determination systems have been proposed that use received signal strength fingerprints to identify locations. We implemented a Bayesian method for location determination in a WLAN testbed and were able to get about 80% accuracy of estimation with a precision of 2.5 meters. We proposed two mechanisms to improve this accuracy: 1) Kalman filtering to remove noise in received signal strength readings and 2) a technique which uses estimates from multiple observers to determine the location. Results from an IEEE 802.11b based implementation of the first method shows that Kalman filtering during the training phase can increase this accuracy to 90%. The multiple observer technique that uses received signal strength readings of the mobile device at the access point, also shows a similar increase in accuracy. Since the multiple observer technique requires more time and resources, we conclude that Kalman filtering is a more efficient and simple way to increase the accuracy of location determination