Daehyun Kim

Mathematical formulation of RFID tag floor based localization and performance analysis for tag placement

A radio-frequency identification (RFID) tag floor based localization is recently proposed indoor mobile robot localization method that utilizes super-distributed RFID tag infrastructure (SDRI) installed on a working area. An RFID tag floor localization (RTFL) method is easy to scale up the working area and the number of robots and is reliable in the position estimation. There have been several researches for practical applications of the RTFL, however, the investigation on performance and properties of the localization method is still insufficient. This paper propose a mathematical formulations of the RTFL and its performance index based on an RFID position estimation error variance. This paper also presents simulation results of the RTFL performances and analysis. These results can be used for optimal installation of the RFID tag floor.

Observability-Based Selection Criterion for Anchor Nodes in Multiple-Cell Localization

The coverage of localization systems for a mobile robot can be expanded by using a multiple-cell structure. In such a structure, there are a number of anchor nodes installed at several positions whose coordinates are known in advance. Therefore, the mobile robot must select some anchor nodes that are suitable for its localization. As a selection criterion for these anchor nodes, the strength of the received ranging signal has been used in the conventional manner, but this approach is highly dependent on the particular environment. In this paper, we focus on the recently proposed localization model for biased chirp spread spectrum ranging. To apply the model to multiple-cell localization, we propose a selection criterion for anchor nodes that is based upon the observability of the model. The proposed criterion assures that the estimated coordinates by the extended Kalman filter can track the actual position of the mobile robot when it is located at the boundary of two adjacent cells.

Localization based on two-stage treatment for dealing with noisy and biased distance measurements

Localization can be performed by trilateration in which the coordinates of a target are calculated by using the coordinates of reference points and the distances between each reference point and the target. Because the distances are measured on the basis of the time-of-flight of various kinds of signals, they contain errors which are the noise and bias. The presence of bias can become a major problem because its magnitude is generally unknown. In this article, we propose an algorithm that combines the Kalman filter (KF) and the least square (LS) algorithm to treat noisy and biased distances measured by chirp spread spectrum ranging defined in IEEE 802.15.4a. By using the KF, we remove the noise in the measured distance; hence, the noise-eliminated distance, which still contains bias, is obtained. The next step consists of the calculation of the target coordinates by using the weighted LS algorithm. This algorithm uses the noise-eliminated distance obtained by using the KF, and the weighting parameters of the algorithm are determined to reduce the effects of bias. To confirm the accuracy of the proposed algorithm, we present the results of indoor localization experiments.

Improving Position Estimation of the RFID Tag Floor Localization with Multiple Recognition Ranges

This chapter introduces the RFID tag floor localization method with multiple recognition ranges and its mathematical formulation to improve position estimation accuracy. Using the multiple recognition ranges of RFID reader, the reader can obtain more information about the distances to the tags on the tag floor. The information is used to improve the position estimation performance. At first, this chapter reviews the RFID tag floor localization methodwith single recognition range formobile robots(Park et al., 2010) and The performance measure based on the position estimation error variance for the localization method. For the second, this paper extends the mathematical formulation of the localization method and the performance measure for the case of multiple recognition ranges. This work is related to the previous work(Park et al., 2009) that used multiple powers to improve position estimation performance. However, previous work lacks analysis and mathematical formulation of general RFID tag recognition models. We extend the mathematical formulation and the analysis of the single recognition range RFID tag floor localizationmethod (Park et al., 2010) to the multiple recognition range case. Then the minimum error variance of multiple recognition range is introduced as a lower bound of position estimation error variance. Finally, it presents performance improvement of proposed localization method via the Monte-Carlo simulation and simple experiments. The analysis for the simulation and experimental results and the consideration for real application will be given. This chapter is organized as follows; This section discusses sensor systems used in the mobile robot localization. Then the advantages of the RFID systems as sensor systems for localization are discussed and the researches on the systems are reviewed. Section 2 introduces the RFID tag floor localization, its mathematical formulation and its performance index. Section 3 represents the motivation of introducing the use of multiple recognition ranges for the RFID tag floor localizationmethod, and extend themathematical formulation and the error variance for the multiple recognition range case. Section 4 conducts the Monte-Carlo simulation to show the improvement of the position estimation performance when the multiple recognition range is used. Section 5 represents experimental results that support the simulation results. In Section 6, the minimum error variance(Park et al., 2010) as a lower bound of error variance is extended to the multiple recognition range case. Section 7 gives the conclusions, discussions and tasks for the further researches. Youngsu Park, Je Won Lee, Daehyun Kim, Sang-woo Kim Electronic and Electric Engineering department, POSTECH Korea, South Improving Position Estimation of the RFID Tag Floor Localization with Multiple Recognition Ranges 10

Grey prediction method for forecasting the capacity of lithium-ion batteries

The battery performance is gradually decreased over its cycle life as a result of capacity loss. This paper proposes a capacity forecasting method of lithium-ion battery pack using a grey predictor. In this method, the forecasted capacity of battery pack is obtained from the previous four measured capacity data. The prediction performance of the proposed method is verified through a comparison with the prediction results from the curve-fitting based method. The experimental results show that the proposed method can accurately predict a one-step-ahead capacity value even with a small number of data.

Robust observer for state-of-charge estimation of li-ion battery with uncertainties

We developed a robust state-of-charge (SOC) estimation algorithm which considers uncertainties of matrix including internal resistance for Li-Ion battery. We used a linear matrix inequality (LMI) to acquire gain of the observer for estimating SOC. The algorithm is less accurate than estimation results of other algorithms, but has simple and fast calculation by using a time-invariant observer gain. This algorithm can contribute to acquire SOC of old battery cells which have the higher internal resistance and uncertainty of the initial battery model.

Model-based iterative position estimation algorithm for RFID tag floor localization

The RFID tag floor localization method is one of the recently proposed localization methods for mobile platforms based on the RFID system. The method utilizes an RFID tag grid on a work area and RFID readers under the mobile platforms for localization. There have been several researches to improve the accuracy of the position estimation from the information of detected tags. However, the accuracy of position estimation is limited by the density of the RFID tag grid. However, with the recognition model of tags and readers, the position estimation accuracy can be improved more. For instance, localization of RFID tags with virtual reference tags (L-VIRT) algorithm utilizes model information to improve the position estimation accuracy, but it requires numerous model evaluations for localization. This paper proposes a more accurate and efficient model-based iterative localization algorithm for the RFID tag floor localization. The performance improvement is verified by simulations.