Final Program

Abstract

A welcome address to open the 2nd annual Workshop on Wireless Motion Capture and Fine-scale Localization as well as an overview for the newly-formed IEEE CRFID Technical Committee on Motion Capture and Localization. A summary of TC-MoCap's mission and future plans are included. These include additional workshops at two more CRFID-sponsored conferences later this year (IEEE RFID-TA and IEEE WiSEE) as well as a special issue call for papers for IEEE Journal on RFID in the area of Motion Capture and Localization. This talk provides an overview of cellphone location technologies in E911 services today. We'll review location accuracy requirements set by FCC and present technologies currently available to enhance location accuracy further, especially in the vertical dimension, also known as the z-axis. RFID our average mean for a and when and moving. We multi-user an average mean of system for locating In this paper, new compressive sensing (CS)-based direction of arrival (DOA) estimation technique using the beamspace (BS) processing is proposed. Two techniques have been proposed, namely, full beam-space (FBS) as well as multiple beam-space (MBS), and investigated versus the ordinary element-space (ES) technique in a CS-based framework. More, the rank one update covariance matrix has been combined along with all the investigated techniques. Both of the proposed schemes can identify more source signals than the number of sensors used, without requiring an a priori knowledge of the number of source signals to be estimated. The performance of the proposed schemes is compared to that of the ES-based technique. This paper presents a RFID-based mobile robot able of self-locating within an indoor scenario and to estimate the position of target UHF-RFID tags. To locate itself, the robot exploits a sensor-fusion method which combines data from an infrastructure of passive reference RFID tags arranged in known locations and data from rotary wheel encoders. Besides, during its motion it is able of measuring the target tag locations through a synthetic-array approach. The knowledge of the reader antenna trajectory is here achieved from the RFID-based sensor-fusion method which exhibits a localization error lower than 0.27 m for 20-m long paths in a real office environment. Then, the estimated trajectory is exploited for target tag localization with high accuracy by using the synthetic-array approach. This work revisits particle filtering RFID localization methods, solely based on phase measurements. The reader is installed on a low-cost robotic platform, which performs autonomously (and independently from the RFID reader) open source simultaneous localization and mapping (SLAM). In contrast to prior art, the proposed methods introduce a weight metric for each particle-measurement pair, based on geometry arguments, robust to phase measurement noise (e.g., due to multipath). In addition, the methods include the unknown constant phase offset as a parameter to be estimated. No reference tags are employed, no assumption on the tags' topology is assumed and special attention is paid for reduced execution time. It is found that the proposed phase-based localization methods offer robust performance in the presence of multipath, even when the tag phase measurements are variable in number and sporadic. The methods can easily accommodate a variable number of reader antennas. Mean absolute localization error, relevant to the maximum search area dimension, in the order of 2% - 5% for 2D localization and 9.6% for 3D localization was experimentally demonstrated with commodity hardware. Mean absolute 3D localization error in the order of 24 cm for RFID tags in a library was shown, even though the system did not exploit excessive bandwidth or any reference tags. As a collateral dividend, the proposed methods also offer a concrete way to classify the environment as multipath-rich or not. Preliminary Analysis of RFID Localization System for Moving Precast Concrete Units using Multiple-Tags and Weighted Euclid Distance k-NN algorithm This paper presents two RFID localization methods based on a k-NN algorithm for multiple moving tracking tags attached to a concrete masonry unit (cinder block). This work uses passive RFID tags for localization and seeks to provide rapid wireless analysis for future smart infrastructure projects where precast concrete modular This paper proposes a new type of real-time decimeter-level radio-frequency identification (RFID) positioning system at 5.8 GHz. The system uses received signal phase (RSP)-based positioning techniques and tunneling tags (TTs). TTs amplify the signal strength of their backscattered signals while preserving the phases allowing for ultra-precise position estimates at long distances. A proof-of-concept RSP-based real-time frequency hopping reader is implemented on Software-Defined Radio (SDR) and Universal Software Radio Peripheral (USRP) platform. Experimental results show an average one dimensional and two-dimensional positioning accuracy of 11 cm and 17 cm, respectively, in outdoor environments. This paper proposes an ultrahigh-frequency (UHF) radio frequency identification (RFID) based 3D mobile localization system (3DRML) for passive tags and tagged objects. Influenced by factors such as calculation model, grid scale and phase center shift (PCS), prior RFID based 2D and 3D mobile localization methods are subject to certain restrictions in computational time and accuracy. To overcome these limitations, 3DRML has the following features. First, 3DRML achieves grid based mobile localization with low time cost by leveraging the idea of reflection coefficient reconstruction (RCR) which regards each point representing an area as a reflection point and calculates the reflection coefficients from simple matrix operations. Second, a PCS calibration process is performed to compensate the phase shift caused by the antenna phase center change. Third, 3DRML uses the nonlinear optimization algorithm to solve the least square localization model for a quick localization, and then constructs a much smaller grid area to facilitate the grid based real-time accurate localization. The performance of 3DRML is evaluated by simulations with various interferences, and the results show that 3DRML enables fast 3D localization while achieving higher accuracy. A Backscatter RFID This paper introduces a backscatter channel sounder technique used for a radio-frequency identification (RFID) positioning system at 5.8 GHz. This system applies received signal phase (RSP)-based positioning and channel sounding techniques to a tunneling tag, providing sufficient information to calculate the delay spectrum for accurate positioning in a complicated multipath environment. Ultra-precise (0.45%) position estimates at long distances (100 m) are achieved using the proposed channel sounding techniques. In this paper we explore two machine learning approaches to improve RFID tag localization in the highly reflective environment imposed by the International Space Station. We propose P-RFIDNet (Passive RFID Net), a neural network with a ResNet50 (He, et al., 2015) [1] backbone for localizing passive RFID tags in high multipath environments with fixed antennas. Furthermore, we show how transfer learning can be used to generalize P-RFIDNet to new RFID environments with limited training data. In addition to P-RFIDNet, we present REALMRFC, a random forest (Breiman, 2001) [2] model with feature engineering performed by an RFID localization expert. We benchmark P-RFIDNet and REALMRFC using data from the RFID Enabled Autonomous Logistics Management (REALM) RFID system on International Space Station (ISS).