 Research
 Open Access
On phasebased localization with narrowband backscatter signals
 Nicolò Decarli^{1}Email author
https://doi.org/10.1186/s1363401805904
© The Author(s) 2018
 Received: 14 December 2017
 Accepted: 17 October 2018
 Published: 12 November 2018
Abstract
Backscatter communication is widely adopted for radiofrequency identification (RFID). Recently, the possibility of localizing passive tags or readers, exploiting phase measurements from backscatter signals, received large attention. In particular, several applications with standard ultrahigh frequency (UHF) RFID were proposed, thanks to the availability of the phase information in many commercial readers, without requiring any hardware modification. In this paper, the problem of localizing a tag or a reader using phase measurements is addressed from the estimation theory point of view. The derived structure for the maximum likelihood estimator is compared with other approaches proposed in the literature, showing its enhanced performance in a typical application context.
Keywords
 Localization
 Phase
 Estimation
 RFID
 Backscatter
1 Introduction
In the last years, the capability of communicating with a passive transponder, thanks to the modulation of the signal backscattered by its antenna, has been exploited to detect and identify cheap and small devices, namely tags [1]. This technique, which is the foundation of passive radiofrequency identification (RFID) systems, became more and more pervasive from the introduction of the secondgeneration standard, working in the ultrahigh frequency (UHF) band [2].
More recently, the possibility of localizing RFID tags has attracted the research attention [3–5]. In fact, offering positioning capabilities to RFID tags would enable a myriad of new applications, thanks to the very low cost and size of such devices, and the absence of a battery. Unfortunately, due to the low complexity of RFID tags, only measurements from a set of the reference nodes (i.e., readers) can be exploited, without considering cooperative techniques [6, 7]. Classical noncooperative localization techniques are usually based on readertag distance estimation, where distance is computed from received signal strength (RSS) or timeofarrival (TOA) measurements [8]. RSSbased techniques, generally considered for their low complexity, offer poor performance, since RSS is not a deterministic monotone function of the distance [8]. Differently, precise TOA estimation is challenging in the RFID context, since the achievable accuracy is related to the signaltonoise ratio (SNR) and to the signal bandwidth, if classical noncoherent estimation techniques are considered [9, 10], and the bandwidth is extremely narrow in this kind of systems [1]. For this reason, ultrawideband (UWB) RFID was also proposed in order to merge the high temporal resolution of wideband signals, beneficial for highaccuracy TOA estimation, with the benefit of backscatter communication in terms of extremely low power [11, 12].
and, of course, combination of the previous ones (e.g., localization of a tag using multiple antennas and multiple radio channels).
Among the aforementioned localization systems and techniques, we can distinguish between the case of moving tag and fixed reader and the case of moving reader and fixed tag. As application for the first case, we can consider the localization of tags carried by items moving on conveyor belts. In this case, the classical problem that can be tackled is the determination of their orderofarrival, then enabling sortation of the goods for automatic dispatch (e.g., luggage in airports or packages in warehouses) [28–31]. In such a scenario, the tags move on a known trajectory (i.e., that defined by the conveyor belt) and one or more readers collect several measurements to determine the relative tags’ locations and, then, the tags’ order. A different application, dual if compared to the previous one from the system setup point of view, is the localization of goods in smart shelves (e.g., books in a library or pallets in industrial racks). In this case, a reader moving along a known trajectory (e.g., on a rail or carried by a robot) can perform several measurements from different locations to determine the position of tags attached to the items placed on the shelves [20, 32, 33].
To the best of the author’s knowledge, no approaches resorting to classical estimation theory were considered, among the contributions available in the literature, to identify the suitable signal processing scheme for inferring the position of the tag (or of the reader), adopting phase measurements. In this paper, the problem of localizing a tag using phase measurements, taken by a reader moving along a known trajectory, is addressed exploiting estimation theory. The structure of the maximum likelihood (ML) estimator is derived, and it is showed that it has a different form with respect to the estimators previously proposed. The same signal processing scheme applies unchanged to the other problems detailed before, such as localization of readers using reference tags or localization of tags’ array. The ML estimator performance is simulated and compared with that of other approaches proposed in the literature showing its enhanced accuracy. Moreover, it is analytically proved that different approaches previously considered resort to the same estimation structure, then presenting equivalent performance.

The analysis of the phasebased localization technique for narrowband RFID, by presenting several examples, insights and practical configurations;

The derivation of the ML estimator structure for the considered problem;

The comparison of the proposed signal processing scheme with others proposed in the literature, showing their performance for the localization of a tag with a moving reader, and the discussion of the different approaches.
The remainder of the paper is organized as follows. Section 2 introduces the system and signal model. Section 3 derives the structure of the ML estimator for localization using phase measurements from narrowband backscatter signals. Section 4 revises other techniques proposed in the literature. Section 5 shows the simulated estimation performance using different techniques, such as the one derived in this paper and others analyzed in Section 4. Finally, Section 6 concludes the paper.
2 System model
2.1 Signal model
where κ is the attenuation coefficient, accounting for the propagation loss, τ=(d_{T}+d_{R})/c is the traveling delay, c is the speed of light, and n(t) is the additive white Gaussian noise (AWGN). Then, we have
where^{2} a_{R}=κ·a_{T} and ϕ=−2πfτ.
Considering a receiver synchronized inphase with the transmitter (e.g., a receiver colocated with the transmitter, as in commercial RFID readers, exploiting the same transmitted signal as reference for demodulation), the received signal can be projected into the inphase and inquadrature directions. Then, the reader measures the inphase and inquadrature amplitudes or, equivalently the magnitude and the phase, by returning a complex sample \(\tilde {a} e^{\varphi }\), with the phase represented in the [0÷2π] range (Fig. 1).
2.2 Geometric aspects
Due to the periodicity of the phase measured by the reader, the sum of the distance d_{T}+d_{R} cannot be directly inferred by observing φ because this would lead to an infinite number of possible distances. Specifically, if we consider a monostatic configuration (a reader with transmitter and receiver colocated), the phase φ describes an infinite set of circles, with center corresponding to the reader’s position and radius equal to \(\frac {\varphi c}{4\pi f} + k \frac {\lambda }{2}\), for k=1,2,…,∞ and λ=c/f indicating the wavelength. Differently, considering a bistatic configuration, the phase φ describes an ellipse, with foci corresponding to the transmitter’s and receiver’s positions, respectively, and semi major axis equal to \(\frac {\varphi c}{4\pi f} + k \frac {\lambda }{2}\). The infinite number of circles/ellipses corresponding to a specific measured phase value makes positioning more challenging with respect to classical distancebased approaches resorting to trilateration, where three measurements are sufficient for unambiguous 2D localization [8]. Figure 2 presents an example of this geometric interpretation of the phasebased localization for a passive tag. Two readers (with transmitter and receiver colocated) are considered in coordinates [−1, 0] (blue) and [1, 0] (red); a tag is considered in [0, 2] (green). Intersection of circles denotes the possible locations of the tag considering one phase measurement per reader (only the first 20 are reported for each reader). In this case, adopting such a couple of measurements, ambiguity cannot be resolved due to the large number of intersections, and localization results unreliable. For these reasons, a (possibly rich) set of phase measurements is exploited in order to minimize ambiguities and making feasible the position estimation^{3}.
Consider now that this set of phase measurements is collected. As detailed in Section 1, measurements can be taken by using different frequencies, multiple antennas, or readers/tags moving along known trajectories. For simplicity of notation, we assume here the case of tag localization, by using multiple measurements taken by a reader, eventually equipped with multiple antennas and/or using different radio channels. The same results apply to the other problems listed in Section 1.
The amplitude \(\tilde {a}_{i}\) and the phase φ_{i} are samples reported by the reader for the ith measurement. Differently, a_{i} and ϕ_{i} are the corresponding noisefree values. Notice that we considered an explicit dependence of the phase ϕ_{i} with p since our goal is to obtain a phasedependent position estimator; differently, the amplitude a_{i} is treated as an unknown deterministic parameter (i.e., a nuisance parameter [34]), not related to the tag position p for the estimation purposes. Such an assumption is reasonable since the measured signal amplitude, that is, the RSS, is known to be a poor positionrelated parameter [8].
The noise vector n= [n_{1} n_{2} … n_{N}]^{T} has independent elements \(n_{i}\sim \mathcal {CN}\left (0,\sigma ^{2}\right)\), which is a circularly symmetric Gaussian random variable [35]. According to this model, each IQ component is Gaussian distributed with variance σ^{2}/2.
2.3 Example of application
 1.It affects the capability of localizing the tag by solving the phase ambiguities, as described in Section 2.2;
 2.
It affects the localization accuracy due to the relative position between the reader and the tag. This effect is usually known as geometric dilution of precision (GDOP), and it is intrinsic of every localization system [8].
Moreover, it can be noticed how the phase behavior changes with the readers’ position. In fact, when the reader is far from the tag we have (x−x_{Ti})^{2}≫(y−y_{Ti})^{2}, and the relation between the measured phase and the reader’s position on the x axis is almost linear (without considering the [0÷2π] representation), that is \({\phi _{i}({\textbf {p}})\approx \frac {4\pi f_{i}}{c} \left (x{x_{\mathrm {T}i}}\right)}\). In this region, we observe a 2π jump of the measured phase approximately every λ/2. Differently, when the reader approaches the tag, by moving along a different direction, the nonlinear relation between the measured phase and the reader’s position along the x axis can be seen in Fig. 5.
The next section will derive the ML estimator for determining the tag position, that is, the scheme to process the collected phase data.
3 Phasebased localization
Now, the ML estimator for the position of the tag, adopting phase measurements taken by the reader, is derived.
As usually assumed, the position can be determined with the discretization of the search space in a grid approach [19], then testing all the possible hypotheses for p, and taking the most probable. In practice, the sequence of measured phase values (6) is properly correlated using (14) with a hypothetical sequence (7) according to the position p under test^{5}.
Notice that, according to (14), each phase measurement is weighted by the received power \(\tilde {a}_{i}^{2}\) returned by the reader. In this manner, the phase values related to higher SNR have a greater impact on the overall likelihood function and contribute heavily to the position estimation.
3.1 Special case: constant amplitudes
where the approximation holds at high SNR.
Notice that, using the ML approach with different signal models, we have obtained a sort of maximal ratio combining (MRC) estimators (through (14) and (16)) or equal gain combining (EGC) estimator (through (17)). The corresponding performance will be presented in Section 5.
4 Other approaches
For comparison purposes, in this paragraph, the obtained ML estimator structure is compared with other approaches already proposed in the literature.
In Section 5 the performance of this estimator will be compared with (14) and (16), since it resorts to both amplitude and phase. Moreover, in order to obtain an estimator exploiting phaseonly measurements, (18) was also proposed by posing \(\tilde {a}_{i}=1, \forall i\). Such estimator, not accounting for the amplitude of the signal, will be compared with (17).
Then, it has been proved that the approach (19), resorting to phase differences reported in (20) and (21), is formally equivalent to (18) with constant amplitudes, then presenting the same performance. In fact, phase is relative because of its periodic behavior, so considering phase differences with respect to a fixed reference (i.e., the first measurement) shall not improve or degrade the performance.
5 Results and discussion
In this section, the performance of the derived ML estimators is compared with that of the approaches already proposed in the literature.
5.1 Numerical setup
We consider a singleantenna reader moving along a rectilinear trajectory on the x axis and taking N = 10 equally spaced measurements between x = 3 m and x = 5 m, with y = 0 m. The tag to be localized is located in p =[5,1].
A singlechannel CW signal is considered for all the interrogations, with frequency f_{i} = 868 MHz, ∀i. Signal amplitude of each sample is simulated according to the twoway path loss in free space. The SNR is defined as average among the different samples constituting the observation vector.
For what concerns the implementation of the algorithms for position estimation, a 1D search along the x axis is considered, by assuming known the tag coordinate y = 1. The search for the tag position along the x axis is performed between x = 2 m and x = 8 m, with step 1 mm.
Results are presented in terms of rootmeansquare error (RMSE) of the tag position estimation obtained among all the MonteCarlo trials. Specifically, 10^{5} trials were considered in the simulations.
5.2 Variable amplitude
As it is possible to notice, at high SNR all the ML estimators outperform the approaches proposed in the literature, regardless the adoption of phaseonly (i.e., (18)) or both amplitude and phase (i.e., (23)). Figure 6 shows that (14) ensures the best performance in the asymptotic region. Similar performance is given by (16); in this case, the small degradation is due to the model mismatch, since the latter was derived from the assumption of constant amplitude signal, which does not correspond to the simulated scenario. A small performance degradation is offered by estimator (17), which does not require the availability of the signal amplitude then leading to a simpler implementation. At high SNR, 3 dB of performance gap is present between (14) (i.e., the MRC approach) and (17) (i.e., the EGC approach).
Comparison between ML estimators and other approaches presented in [14, 19] shows the enhanced performance of the first group. In particular, if we consider the asymptotic region, an error of 10^{−3} m can be obtained with 20 dB SNR for (14), and 35 dB SNR for (18) (not displayed for space constraints), resulting in a 15 dB gap. Similarly, at 20 dB SNR, the difference between the two approaches is of about 5 times on the achievable RMSE.
In general, it is possible to notice the classical behavior of nonlinear estimators, with different operating regions at different SNR [34, 35]. At low SNR, the performance becomes quickly quite poor for all the estimators. In this region, approach (18) has a performance slightly improved with respect to the ML estimators. The explanation of such a phenomenon can be found in the structure of the functions that are maximized. Figure 7a shows the likelihood function (14), in the absence of measurement noise. Differently, the function (18) is depicted in Fig. 7b. It is possible to notice the sharpness of the main lobe of (14), for which the ML is expected to work very well in the high SNR region, where the probability of selecting the correct lobe is high.^{6} Differently, the performance of (18) in this region is intrinsically poorer, due to the larger main lobe. The situation swaps at low SNR, where the noise could lead to ambiguities in the selection of the main lobe of (14); in this case a smoother behavior, as reported in Fig. 7b, could be beneficial.
5.3 Constant amplitude
5.4 Effects of the tag position
It is possible to notice that the derived ML outperforms the other approach for every tag position at the selected SNR of 15 dB. Depending on the tag position, up to one order of magnitude of difference in the localization accuracy is experienced (e.g., for x = 2 m or x = 6 m). Differently, when the tag is located along the direction orthogonal to the middle point of the synthetic aperture described by the reader, the two approaches presents the same performance. Notice that uniform spatial samples along the synthetic aperture where considered in the simulation.
6 Conclusion
This paper presented the structure of the ML estimator for the position of a RFID tag, using phase measurements. The derivation was conducted under different models of the received signal, in order to obtain estimators exploiting only the phase information, or both amplitude and phase. The derived estimators were compared with the approaches already proposed in the literature, showing the performance improvement that can be obtained using the proposed signal processing scheme. Moreover, it has been proved that other approaches presented in the literature lead to the same estimation structure, then presenting equivalent performance.
It has been shown how approaches that properly weights the phase information with the received signal amplitude ensure the best performance. In the high SNR regime, the use of the ML estimator corresponds to an improvement of up to one order of magnitude in the estimation error for certain tag positions. Differently, at low SNR the performance of all the estimators is similar and ambiguities limit severely the effectiveness of these positioning schemes.
Future research attention should be devoted in determining the design criteria of these positioning schemes, since many aspects affect the feasibility of the localization and the performance, as the number of measurements and their position (i.e., the spatial sampling), frequency, tag position, and number of antennas. Finally, the characterization of the effects of multipath propagation, which can severely affect the phase information, should be investigated, also considering experimental studies in real scenarios.
Additional phase shifts due to the circuits at reader and tag side [13] are supposed calibrated out.
Notation s_{i}(p,a_{i}) denotes that the sample s_{i} is a function of the tag position p, to be estimated, and of the unknown parameter a_{i}.
Declarations
Acknowledgements
The author would like to thank F. Guidi for the fruitful discussions and carefully reading the manuscript, and the anonymous reviewers for the constructive comments provided.
Funding
This research has been supported, in part, by the European Space Agency project LOST and by the EU Horizon 2020 research and innovation programme under the project XCycle (grant no. 635975).
Availability of data and materials
Not applicable.
Authors’ contributions
The author read and approved the final manuscript.
Competing interests
The author declares that he has no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 K. Finkenzeller, RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification, 3rd ed. (Wiley, 2010).Google Scholar
 EPCGlobal, Class 1 generation 2 UHF air interface protocol standard v.1.0.9. (2005).Google Scholar
 A. Costanzo, D. Masotti, T. Ussmueller, R. Weigel, Tag, you’re it: ranging and finding via RFID technology. IEEE Microw. Mag. 14(5), 36–46 (2013).View ArticleGoogle Scholar
 R. Miesen, R. Ebelt, F. Kirsch, T. Schafer, G. Li, H. Wang, M. Vossiek, Where is the tag?. IEEE Microw. Mag. 12(7), 49–63 (2011).View ArticleGoogle Scholar
 L. M. Ni, D. Zhang, M. R. Souryal, RFIDbased localization and tracking technologies. IEEE Wirel. Commun. 18(2), 45–51 (2011).View ArticleGoogle Scholar
 H. Wymeersch, J. Lien, M. Z. Win, Cooperative localization in wireless networks. Proc. IEEE. 97(2), 427–450 (2009).View ArticleGoogle Scholar
 B. Etzlinger, F. Meyer, F. Hlawatsch, A. Springer, H. Wymeersch, Cooperative simultaneous localization and synchronization in mobile agent networks. IEEE Trans. Signal Process. 65(14), 3587–3602 (2017).MathSciNetView ArticleGoogle Scholar
 D. Dardari, E. Falletti, M. Luise, Satellite and terrestrial radio positioning techniques  a signal processing perspective (Elsevier Ltd, London, 2011).Google Scholar
 C. W. Helstrom, Statistical theory of signal detection, 2nd ed. (Oxford: Pergamon Press, Headington Hill Hall, 1968).MATHGoogle Scholar
 D. Dardari, A. Conti, U. Ferner, A. Giorgetti, M. Win, Ranging with ultrawide bandwidth signals in multipath environments. Proc. IEEE. 97(2), 404–426 (2009).View ArticleGoogle Scholar
 N. Decarli, F. Guidi, D. Dardari, Passive UWB RFID for tag localization: architectures and design. IEEE Sensors J. 16(5), 1385–1397 (2016).View ArticleGoogle Scholar
 D. Dardari, N. Decarli, A. Guerra, F. Guidi, in IEEE Int. Conf. on RFID. The future of ultrawideband localization in RFID, (2016), pp. 1–7.Google Scholar
 P. V. Nikitin, R. Martinez, S. Ramamurthy, H. Leland, G. Spiess, K. V. S. Rao, in IEEE Int. Conf. RFID. Phase based spatial identification of UHF RFID tags, (2010), pp. 102–109.Google Scholar
 A. Buffi, P. Nepa, F. Lombardini, A phasebased technique for localization of UHFRFID tags moving on a conveyor belt: performance analysis and testcase measurements. IEEE Sensors J. 15(1), 387–396 (2015).View ArticleGoogle Scholar
 A. Buffi, M. R. Pino, P. Nepa, Experimental validation of a SARbased RFID localization technique exploiting an automated handling system. IEEE Antennas Wirel. Propag. Lett. PP(99), 11–1 (2017). early access online.Google Scholar
 M. Wegener, D. Fro, M. Rler, C. Drechsler, C. Ptz, U. Heinkel, in Int. Multiconference on systems, signals devices. Relative localisation of passive UHFtags by phase tracking, (2016), pp. 503–506.Google Scholar
 M. Vossiek, A. Urban, S. Max, P. Gulden, Inverse synthetic aperture secondary radar concept for precise wireless positioning. IEEE Trans. Microw. Theory Tech. 55(11), 2447–2453 (2007).View ArticleGoogle Scholar
 R. Miesen, F. Kirsch, M. Vossiek, in IEEE Int. Conf. RFID. Holographic localization of passive UHF RFID transponders, (2011), pp. 32–37.Google Scholar
 R. Miesen, F. Kirsch, M. Vossiek, UHF RFID localization based on synthetic apertures. IEEE Trans. Autom. Sci. Eng. 10(3), 807–815 (2013).View ArticleGoogle Scholar
 R. Zhao, Q. Zhang, D. Li, H. Chen, D. Wang, in Int. symp. world of wireless, mobile and multimedia networks. A novel accurate synthetic aperture RFID localization method with high radial accuracy, (2017), pp. 1–9.Google Scholar
 L. Shangguan, Z. Yang, A. X. Liu, Z. Zhou, Y. Liu, STPP: Spatialtemporal phase profilingbased method for relative RFID tag localization. IEEE/ACM Trans. Networking. 25(1), 596–609 (2017).View ArticleGoogle Scholar
 X. Fu, A. PedrossEngel, D. Arnitz, M. S. Reynolds, in IEEE SENSORS. Simultaneous sensor localization via synthetic aperture radar (SAR) imaging, (2016), pp. 1–3.Google Scholar
 E. DiGiampaolo, F. Martinelli, Mobile robot localization using the phase of passive UHF RFID signals. IEEE Trans. Ind. Electron. 61(1), 365–376 (2014).View ArticleGoogle Scholar
 M. Scherhufl, M. Pichler, A. Stelzer, UHF RFID localization based on phase evaluation of passive tag arrays. IEEE Trans. Instrum. Meas. 64(4), 913–922 (2015).View ArticleGoogle Scholar
 M. Scherhufl, M. Pichler, A. Stelzer, UHF RFID localization based on evaluation of backscattered tag signals. IEEE Trans. Instrum. Meas. 64(11), 2889–2899 (2015).View ArticleGoogle Scholar
 S. Sarkka, V. V. Viikari, M. Huusko, K. Jaakkola, Phasebased UHF RFID tracking with nonlinear kalman filtering and smoothing. IEEE Sensors J. 12(5), 904–910 (2012).View ArticleGoogle Scholar
 C. Zhou, J. D. Griffin, Accurate phasebased ranging measurements for backscatter RFID tags. IEEE Antennas Wirel. Propag. Lett. 11:, 152–155 (2012).View ArticleGoogle Scholar
 L. Shangguan, Z. Yang, A. X. Liu, Z. Zhou, Y. Liu, in 12th USENIX Symposium on networked systems design and implementation (NSDI 15). Relative localization of RFID tags using spatialtemporal phase profiling (USENIX AssociationOakland, CA, 2015), pp. 251–263.Google Scholar
 L. Shangguan, Z. Li, Z. Yang, M. Li, Y. Liu, J. Han, OTrack: Towards order tracking for tags in mobile RFID system. IEEE Trans. Parallel Distrib. Syst. 25(8), 2114–2125 (2013).View ArticleGoogle Scholar
 S. Bartoletti, N. Decarli, A. Guerra, F. Guidi, D. Dardari, A. Conti, Order of arrival estimation via UHFUWB RFID (IEEE International Conference on Communications Workshops (ICC), Sydney, 2014).View ArticleGoogle Scholar
 A. Buffi, P. Nepa, The SARFID technique for discriminating tagged items moving through a UHFRFID gate. IEEE Sensors J. 17(9), 2863–2870 (2017).View ArticleGoogle Scholar
 J. Wang, D. Katabi, in Proceedings of the ACM SIGCOMM 2013 conference. Dude, where’s my card?: RFID positioning that works with multipath and nonline of sight (ACMNew York, 2013), pp. 51–62.View ArticleGoogle Scholar
 J. Liu, F. Zhu, Y. Wang, X. Wang, Q. Pan, L. Chen, in Proc. of the IEEE conf. on computer communications. RFscanner: Shelf scanning with robotassisted RFID systems, (2017), pp. 1–9.Google Scholar
 H. L. V. Trees, Detection, Estimation, and Modulation Theory: Part I, 2nd ed. (Wiley, New York, 2001).MATHGoogle Scholar
 S. M. Kay, Fundamentals of statistical processing: estimation theory (PrenticeHall Signal Processing Series, 1993). vol. 1.Google Scholar
 Y. Shen, M. Z. Win, Fundamental limits of wideband localization – Part I: a general framework. IEEE Trans. Inf. Theory. 56(10), 4956–4980 (2010).MathSciNetView ArticleGoogle Scholar
 J. Kimionis, A. Bletsas, J. N. Sahalos, Increased range bistatic scatter radio. IEEE Trans. Commun. 62(3), 1091–1104 (2014).View ArticleGoogle Scholar
 N. Decarli, F. Guidi, D. Dardari, A novel joint RFID and radar sensor network for passive localization: design and performance bounds. IEEE Sel. J. Topics Signal Process. 8(1), 80–95 (2014).View ArticleGoogle Scholar
 S. Schwarzer, M. Vossiek, M. Pichler, A. Stelzer, in IEEE Radio and Wireless Symposium. Precise distance measurement with IEEE 802.15.4 (zigbee) devices, (2008), pp. 779–782.Google Scholar