Theresa Nick

Camera-assisted localization of passive RFID labels

For indoor localization systems, Radio Frequency Identification (RFID) is an often chosen technique. A passive UHF RFID label can be localized via Received Signal Strength Indicator (RSSI) values using a Constrained Unscented Kalman Filter (CUKF). A camera-based localization technique which employs back projection method and movement estimation is combined with the RFID-based localization. This camera-assisted localization technique leads to an increase in localization accuracy by a factor of two compared to the Constrained Unscented Kalman Filter without camera assistance which already performes twice as good as the Unscented Kalman Filter (UKF).

Localization of UHF RFID labels with reference tags and Unscented Kalman Filter

To accomplish location-awareness in indoor scenarios Radio Frequency Identification (RFID) tags are often used for the localization. This paper only uses Received Signal Strength Indicator (RSSI) values from passive UHF RFID labels for the localization process. The RSSI values of reference tags (RTs) for which the locations are known are employed as side information for the localization of an RFID label (LT). For the localization of this LT an Unscented Kalman Filter (UKF) in combination with the RTs information is used. Simulations based on measured data achieve a localization accuracy of about 17cm with this localization method, compared to a localization error of 20cm without RTs and 83cm when only using k nearest neighbor (kNN) algorithm.

Comparison of extended and Unscented Kalman Filter for localization of passive UHF RFID labels

Due to the increased use of Radio Frequency Identification (RFID) in different fields of application it is reasonable to explore the benefit that can be obtained by the simultaneous localization of RFID tags. This paper describes the localization of a passive UHF RFID tag via Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) using the Received Signal Strength Indicator (RSSI) values. Simulation results based on measurements show that UKF achieves higher localization accuracies than EKF.

Localization based on fusion of RFID and stereo image data

A system that localizes and identifies trolleys in the entry area of a distribution center is presented in this paper. The area is monitored by an RFID reading system (consisting of four antennae and a fixed RFID reader) and an inexpensive pair of webcams. The trolleys are equipped with a passive RFID tag and an optical marker. The system uses a Particle Filter to combine Received Signal Strength Indication (RSSI) data from the RFID system and image data from the webcams. We propose procedures to improve RFID and camera localization accuracy. The resulting system achieves an accuracy of less than 30 cm in static and the most likely dynamic scenarios.

RSS-based channel measurements and their influence on localization in RFID applications

Radio Frequency Identification (RFID) is a valid choice to achieve indoor localization simultaneously to the identification of objects. Measurements of Received Signal Strength (RSS) values show a higher variance for larger distances between tag and reader antenna. Therefore using a variable measurement noise in the localization algorithm is proposed. For the Constrained Unscented Kalman Filter (CUKF) the localization error is reduced by about 10%.

Localization of passive UHF RFID label with Unscented Kalman Filter based on angle-dependent RSSI measurements

For indoor localization systems Radio Frequency Identification (RFID) is an often chosen technique. This paper uses Received Signal Strength Indicator (RSSI) values from passive UHF RFID labels for the localization process. Based on the measurements of these RSSI values a formula is derived to describe the relation between distance from tag to antenna as well as its dependency on the angle between tag and antenna. Two different methods how to incorporate the angle-dependency into the localization process are introduced. Applying these methods an Unscented Kalman Filter (UKF) is used for localization. In our demonstration environment localization accuracy is improved by roughly a factor two when three antennae are used.

Fuzzy-Adaptive Kaiman Filter for RFID localization

Radio Frequency Identification (RFID) can not only be used to identify objects, but also to localize them. If Received Signal Strength Indicator (RSSI) values are converted into distances, a Constrained Unscented Kaiman Filter (CUKF) can estimate an objects position via these measurements. In case of unknown or varying measurement noise a Fuzzy-Adaptive version of the filter (FACUKF) leads to an increase in location accuracy and filter consistency.