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

Nick, Theresa; Goetze, Juergen; John, W.; Stoenner, Gerhard

doi:10.1109/ursigass.2011.6050572

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…In the paper [7], Julier and Uhlmann demonstrated that the UKF performs better than the EKF. Many research later adopted this method in various non-linear filtering and reported the same conclusion, see e.g., [11]. In this work, we study the possibility of using sensor fusion from a low cost IMU and RSSIs by applying the UKF for mobile unit localization in two-dimensional plane.…”

Section: Introductionmentioning

confidence: 70%

RSSI/IMU sensor fusion-based localization using unscented Kalman filter

Malyavej

Udomthanatheera

2014

The 20th Asia-Pacific Conference on Communication (APCC2014)

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The most crucial problem in navigation system is localization. The global positioning system (GPS) has long been used in mobile unit localization. However, GPS is incapable in some situation such as indoor environment. The received signal strength indicator (RSSI) from wireless communication is a promising alternative method to derive the location of a mobile unit. To improve the precision and robustness in the GPS-based localization, an inertial measurement unit (IMU) is normally used. In this paper, we study the possibility to use RSSI from wireless local area network (WLAN) and an IMU to derive the location of the mobile unit. We apply unscented Kalman filter (UKF) as a fusion engine for those two information. The experiment is conducted by using mobile unit equipped with low-cost IMU and a wireless communication module together with multiple access points to evaluate the performance of our algorithm, and the result is promising.

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Section: Introductionmentioning

confidence: 70%

RSSI/IMU sensor fusion-based localization using unscented Kalman filter

Malyavej

Udomthanatheera

2014

The 20th Asia-Pacific Conference on Communication (APCC2014)

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“…The UKF was chosen for use as opposed to the EKF because of its relative ease of computation (no Jacobian calculations), and because of its superior performance compared to the EFK [6], [7], [9], [12]- [14]. The system function, f , state transition matrix, A, and process noise, w, are identical to the KF.…”

Section: B Non-linear Kalman Filter (Ukf) Designmentioning

confidence: 99%

“…Using the readers a system can track the location of a tag within the grid using TOA. Non-linear KF have been applied to such systems and found to reduce noise and increase localization accuracy [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Kalman filters for reducing error in RFID real-time localization systems

Sanda¹,

Abdel‐Qader²,

Akanmu

2014

IEEE International Conference on Electro/Information Technology

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The use of Radio Frequency Identification (RFID) has become widespread in industry as a means to quickly and wirelessly identify and track packages and equipment. Now there is a commercial interest in using RFID to provide real-time localization. Efforts to use RFID technology for this purpose experience localization errors due to noise and non-line-of-sight effects inherent to these environments. This paper presents the use of both linear and non-linear Kalman filters to reduce error effects inherent to real-time RFID localization systems and provide more accurate localization results in indoor environments. A commercial RFID localization system designed for use by the construction industry is used in this work, and a mathematical model and approach are developed. The model is tested with real-world data and shown to provide an increase in localization accuracy when applied to both raw distance difference of arrival measurements as well as final trilateration results.

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“…Van Dyke et al [4], Sadhu et al [5], Orderud [6], Wang et al [7], Won et al [8], and Nick et al [9] reported that the UKF performs significantly and consistently better than the EKF in applications of dual estimation [10] for spacecraft attitude state and parameter estimation, bearing-only tracking, again bearing-only tracking, radar tracking, monocular vision-based inertial navigation system (INS), and localization of radiofrequency identification tags, respectively. Kandepu et al [11] presented the same conclusions through four different simulation studies of the following problems: Van der Pol oscillator, estimation in an induction machine, state estimation of a reversible reaction, and a solid oxide fuel cell combined gas turbine hybrid system.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Extended and Unscented Kalman Filters for Attitude Estimation

Rhudy

Gross

et al. 2013

Journal of Aerospace Information Systems

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The extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nonlinear state estimation with both additive and nonadditive noise structures are presented and compared. Three different Global Positioning System (GPS)/inertial navigation system (INS) sensor fusion formulations for attitude estimation are used as case studies for the nonlinear state estimation problem. A diverse set of actual flight data collected from research unmanned aerial vehicles was used as empirical data for this study. Roll and pitch estimation results were compared with independent measurements from a mechanical vertical gyroscope to evaluate the performance. The performance of the EKF and UKF is compared in terms of noise assumptions, covariance matrix tuning, sampling rate, initialization error, GPS outages, robustness to inertial measurement unit bias and scale factors, and linearization. Similar sensitivity for this GPS/INS attitude estimation problem was found between the EKF and UKF for most cases. Small differences were seen between EKF and UKF for initialization error and GPS outages: the UKF was found to be more robust to inertial measurement unit calibration errors, and the EKF was determined to be more computationally efficient.

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

Cited by 11 publications

References 10 publications

RSSI/IMU sensor fusion-based localization using unscented Kalman filter

RSSI/IMU sensor fusion-based localization using unscented Kalman filter

Kalman filters for reducing error in RFID real-time localization systems

Sensitivity Analysis of Extended and Unscented Kalman Filters for Attitude Estimation

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