The future of ultra-wideband localization in RFID

Dardari, Davide; Decarli, Nicolò; Guerra, Anna; Guidi, Francesco

doi:10.1109/rfid.2016.7487998

Cited by 35 publications

(25 citation statements)

References 30 publications

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“…2d( X) (12) Equation (10) shows that the bias is in nonlinear least-squares estimator of the distance equations is driven by the covariance matrix σ 2 0 Q X and the second order derivatives matrix ∂ 2 d( X). It means that the bias due to nonlinearity is related to both the system geometry and the precision of the estimator.…”

Section: The Bias In the Nonlinear Least-squares Solutionmentioning

confidence: 99%

“…It means that the bias due to nonlinearity is related to both the system geometry and the precision of the estimator. Equation (12) indicates that the bias in the nonlinear least-squares estimator is inversely proportional to the ranging distance and is proportional to the ranging error. The bias bX will be small if b is sufficiently close to zero, so that the model is essentially linear, or if b is orthogonal to the column space of design matrix J( X).…”

Section: The Bias In the Nonlinear Least-squares Solutionmentioning

confidence: 99%

“…Meanwhile, to ensure people and objects can be localized in real-time, the integration of UWB and other technologies such as RFID and Wi-Fi are developed. The passive UWB RFID based on the backscatter modulation signal increases significantly the performance for tags detection and localization at low and complexity by employing energy sprinklers [10][11][12]. The integration of UWB and Wi-Fi are implemented for localization, the Wi-Fi networks guarantee broad coverage while the UWB would increase the overall system accuracy and robustness [13].In practical applications, an unbiased estimation of the positioning result will be expected.…”

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confidence: 99%

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Bias Analysis of Parameter Estimator Based on Gauss-Newton Method Applied to Ultra-Wideband Positioning

Wang

et al. 2019

Applied Sciences

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Ultra-wideband (UWB) is considered as a promising technology in short-distance indoor wireless positioning due to its accurate time resolution and good penetration through objects. Since the functional model of UWB positioning is nonlinear, the optimal solution is generally estimated by the way of continuous iteration. As an iterative descent method of high efficiency, the Gauss–Newton method is widely used to estimate the position. The nonlinear distance equations are linearized, and the solution can be found iteratively. Therefore, the nonlinear least-squares solution is generally biased even if the observations are normally distributed. In outdoor satellite positioning, the ranging distances are long enough so that the bias caused by nonlinearity is very small. However, in UWB positioning, the relative ranging error is large, and the positioning system is prone to become ill-posed, hence the bias due to nonlinearity is not negligible. In this study, both the statistical factor and geometric factor for bias in the nonlinear least-squares estimator of UWB positioning are discussed. In order to assess whether the linearized model is sufficiently approximate for the positioning estimation, a hypothesis test criterion based on Mahalanobis distance is proposed. The simulation and measurement experiments are performed to analyze the factors affecting the bias in UWB positioning. Experimental results are given to demonstrate that the linearization is valid and the bias in UWB positioning estimation can be neglected for the relatively high measurement precision. Moreover, for a positioning configuration, when the anchors are evenly distributed, the amount of nonlinearity is orthogonal to the ranging space of the design matrix, the UWB positioning estimation tends to be unbiased. Meanwhile, the hypothesis test based on Mahalanobis distance is carried out to determine the validity of the linearized model. When the bias is large for UWB positioning, the bias estimate can be used to correct the estimator to guarantee the unbiasedness for UWB positioning. Furthermore, the correction of parameter estimator bias is more effective in the case of relatively low measurement precision or ill-conditioned configuration.

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Section: The Bias In the Nonlinear Least-squares Solutionmentioning

confidence: 99%

Section: The Bias In the Nonlinear Least-squares Solutionmentioning

confidence: 99%

mentioning

confidence: 99%

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Bias Analysis of Parameter Estimator Based on Gauss-Newton Method Applied to Ultra-Wideband Positioning

Wang

et al. 2019

Applied Sciences

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“…We consider the PF algorithm for BF, which can outperform the extended Kalman filter (EKF) in non-Gaussian noisy ob- servations [43], [44]. 7 In particular, the position belief at time i,j,m between the tag and the reader as illustrated in Fig. 6.…”

Section: B Empirical Measurement Modelsmentioning

confidence: 99%

“…Current solutions employ barcode laser scanners, camera barcode readers, vision systems, and RFID readers [5]- [7]. Localization with Gen-2 ultra-high frequency (UHF) RFIDs is based on received signal strength indicator (RSSI), phase (also at different operating frequencies), or angle-of-arrival (AOA) measurements [8]- [14].…”

Section: Introductionmentioning

confidence: 99%

Order-of-Arrival of Tagged Objects

Bartoletti

Decarli

Dardari

et al. 2018

IEEE J. Radio Freq. Identif.

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The order-of-arrival (OOA) of a stream of objects moving along a monitored direction is important for many decision operations in logistic and industrial applications. As an example, the dispatch of luggage in airports requires to determine their OOA at checkpoints; as another example, the automatic steering of items in supply chains requires to sort and identify them while moving on a conveyor belt. This paper establishes a general framework for determining the OOA of objects that are moving along a monitored direction via radiofrequency identification (RFID) systems. Three techniques for OOA tracking are proposed and developed for objects equipped with RFID tags possessing ranging capabilities. The benefits of the proposed techniques are experimentally validated based on measurements gathered with a RFID system operating in an indoor environment.

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Experimental platform for waveform optimization in passive UHF RFID systems

Merakeb

Ezzeddine

Huillery

et al. 2020

Int J RF Microw Comput Aided Eng

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The article presents an experimental platform, so called “RFID Waveformer,” dedicated to the study of waveform optimization in the radio frequency identification (RFID) context. It is a flexible solution that enables waveform design and their energetic performance evaluation in a ultra high frequency (UHF) RFID link following the ISO‐18000 GEN2 standard protocol. It consists of a reader emulated by a LabVIEW interface controlling radiofrequency laboratory instruments, which perform real time tag response detection. Its interconnection with MATLAB routines enables the design and the evaluation of arbitrarily shaped RFID waveforms. In this article, for illustration, three waveforms are tested with RFID Waveformer in a complex propagation environment: pulsed wave (PW) and time reversal (TR) modes compared to the traditional continuous wave (CW) mode. Experimental results show that both PW and TR modes improve the energetic efficiency of the forward link and so the RFID read range compared to CW mode. Furthermore, TR presents the optimal efficiency in complex propagation medium. The RFID Waveformer enables tag response detection in the three modes offering ease of use and repeatability of measurements. The RFID Waveformer being not limited to the scenario considered in this article, it is a versatile solution extendable to other contexts.

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The future of ultra-wideband localization in RFID

Cited by 35 publications

References 30 publications

Bias Analysis of Parameter Estimator Based on Gauss-Newton Method Applied to Ultra-Wideband Positioning

Bias Analysis of Parameter Estimator Based on Gauss-Newton Method Applied to Ultra-Wideband Positioning

Order-of-Arrival of Tagged Objects

Experimental platform for waveform optimization in passive UHF RFID systems

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