Robust location tracking using a dual layer particle filter

Abstract

Location awareness is an important part of many ubiquitous computing systems, but a perfect location system does not exist yet. Among many location tracking systems, we choose the radio frequency identification (RFID) system due to its various applications. However, the received signal strength indicator (RSSI) signals are too sensitive to the direction of the RFID reader’s antenna, the orientation of the RFID tag, human interference, and the diversity of propagation media that might be present. As a result, the direct use of a conventional particle filter does not provide satisfactory tracking performance. To overcome this problem, we suggest a dual layer particle filter, where the lower layer determines the tag’s location in the block level using a triangulation technique or the support vector machine (SVM) classifier, and the upper layer accurately estimates the tag’s location using the conventional particle filter within the pre-computed or classified block. This layered structure improves the location estimation and the tracking performance, because the location evidence from the lower layer effectively restricts the range of possible locations of the upper layer. We implement the proposed location tracking method using a ubiquitous RFID wireless network in an intelligent office, where several RFID readers are located in fixed locations and people or objects with active RFID tags move around the office. Extensive experiments show that the proposed location tracking method is so precise and robust that it is a good choice for person or object tracking in ubiquitous computing contexts. We also validate the usefulness of the proposed location tracking method by implementing it for a real-time people monitoring system in a noisy and complex steel mill.