 Research Article
 Open Access
TOA Estimator for UWB Backscattering RFID System with Clutter Suppression Capability
 Chi Xu^{1}Email author and
 Choi L. Law^{1}
https://doi.org/10.1155/2010/753129
© C. Xu and C. L. Law. 2010
 Received: 23 November 2009
 Accepted: 9 May 2010
 Published: 10 June 2010
Abstract
Time of arrival (TOA) estimation in multipath dense environment for UWB backscattering radio frequency identification (RFID) system is challenging due to the presence of strong clutter. In addition, the backscattering RFID system has peculiar signal transmission and modulation characteristics, which are considerably different from conventional communication and localization systems. The existing TOA estimators proposed for conventional UWB systems are inappropriate for the backscattering RFID system since they lack the required clutter suppression capability and do not account for the peculiar characteristics of backscattering system. In this paper, we derive a nondataaided (NDA) least square (LS) TOA estimator for UWB backscattering RFID system. We show that the proposed estimator is inherently immune to clutter and is robust in undersampling operation. The effects of various parameter settings on the TOA estimation accuracy are also studied via simulations.
Keywords
 Mean Absolute Error
 Delay Spread
 Uplink Channel
 Generalize Maximum Likelihood
 Channel Delay Spread
1. Introduction
Backscattering radio frequency identification (RFID) is a type of RFID technology employing tags that do not generate their own signals but reflect the received signals back to the readers. It is widely used in asset tracking, inventory management, health care monitoring, and other fields [1]. Nowadays many RFID applications such as contextaware healthcare require accurate location information with extended operating range. The conventional backscattering RFID system using continuous wave (CW), however, cannot fulfill this requirement due to its poor distance estimation accuracy and limited operating range. Recently, ultra wideband (UWB) signal emerges as a viable solution for the new generation of backscattering RFID system. UWB is defined by Federal Communications Commission (FCC) as signals having a fractional bandwidth larger than 20% or absolute bandwidth of more than 500 MHz [2]. Such enormous bandwidth brings many advantages such as higher ranging accuracy, lower probability of interception, and more resistance to multipath fading as compared to CW signal [3]. Its capability in achieving accurate range and location estimation has been proven analytically and in experiments [4–7]. UWB signal has been applied to backscattering RFID system with localization functionality in [8, 9]. In [8], the concept of UWB pseudorandom backscattering tag is introduced. The tag receives signal only in certain time interval according to a pseudorandom time hopping sequence, slightly delays it and then reflects it back to the reader. In [9], a UWB backscattering RFID tag that is able to apply various modulation schemes is proposed and its potential operating range/data rate tradeoff in the presence of strong clutter is investigated.
Ranging for UWB backscattering RFID system requires estimation of time of arrival (TOA) of the tag response signal at the reader. However, recovering TOA information in environments with dense scatterers is challenging due to the undesired background clutter caused by scatterers other than RFID tags [10]. Clutter paths arriving earlier than the direct path of the desired tag response signal may cause intolerable false alarm rate whereas those overlapping with the direct path may distort its pulse shape. As a result, the estimation accuracy can be severely degraded. To clean the received signal contaminated by clutter before applying any TOA estimation algorithm, the emptyroom or frametoframe techniques used in the radar community may be applied [11]. The emptyroom technique subtracts the channel response measured in the absence of the target from any received signal comprising of both target response and clutter. Unfortunately, this technique is sensitive to environment changes since any change renders the previously measured emptyroom response inappropriate for further use. For the frametoframe technique, each measurement consists of two received signals captured at different timings. The technique subtracts one signal from the other to eliminate the clutter that is assumed to be static in each measurement but may change from measurement to measurement. This technique fails if the target does not move or moves little between received timings of the two signals in the same measurement. Besides the emptyroom and frametoframe techniques, it is also possible to mitigate clutter by proper selection of the modulation sequence used by the tags. In [9], it is shown that if the tag's modulation sequence for antipodal 2PAM signaling fulfills certain criteria, by averaging over multiple symbols, the received signal is immune to clutter. The study, however, is carried out in the context of data communication under the assumption that perfect pulse synchronization to the first arriving path is achieved, that is, TOA information is known.
There are many existing TOA estimators proposed for UWB system. The channel estimators derived in [12, 13] are able to estimate the delays of the paths and hence can be implicitly used as TOA estimators. A generalized maximum likelihood (GML) estimator is proposed in [14], which performs the channel estimation in a predefined time interval prior to the largest sample and takes the timing of the first channel tap crossing a preset threshold as TOA. The subspacebased TOA estimators are pursued in [15, 16]. Frequencydomain superresolution TOA estimation with diversity techniques is studied for indoor localization applications in [17]. The computational complexity of the above estimators is high due to their requirements of either estimation of large number of multipath, or eigen value decomposition of matrices with large dimension. Recent works of TOA estimation focus on the development of lowcomplexity algorithms. A few suboptimal TOA estimation algorithms with reduced computational complexity are introduced in [18, 19]. The cyclostationarity nature of UWB signal is exploited to develop nondataaided (NDA) TOA estimators [20, 21]. A novel "timing with dirty template" (TDT) synchronization criterion is established in [22] based on which both dataaided (DA) and NDA estimators are derived. As shown in [23], it is also possible to transform timing estimation into a maximum likelihood (ML) amplitude estimation problem and derive a closed form solution for the frame level timing offset. A few thresholdcrossing TOA estimators applicable for energy detection (ED) receiver are developed in [24–26]. In [24], a normalized threshold selection adapted to the strength of the received channel profile is proposed. In [25], a threshold selection method based on Kurtosis value is developed which is proven to be robust to channel condition variation. In [26], the threshold is set as a function of propagation delay and simulation results show that considerable performance improvement can be achieved over conventional methods. A TOA estimator allowing long energy integration duration is derived based on unknown pulse shape and GML criterion [27]. Based on least square (LS) criterion, TOA estimators for UWB system are derived in [28, 29]. To speed up the estimation process, different twostage estimators are proposed where the first stage estimates a coarse TOA and the second stage refines the result [30, 31]. An excellent literature review of UWB TOA estimation is presented in [32]. All these TOA estimators, however, are derived in the context of conventional UWB system which involves oneway channel propagation and the signal modulations are done at the transmitter. Therefore, the channel response to a single transmitted UWB pulse only contains the information of a single data bit. The UWB backscattering RFID system, however, presents a different propagation scenario. It involves roundtrip channel propagation and the signal modulation is performed at the tag which lies in the middle of the roundtrip channel. If the clock of the backscattering tag does not synchronize with the clock of the reader, such modulation incurs mismatch, that is, the front part of a channel response received at the tag may be modulated by one bit while the tail is modulated by the next data bit. Hence the received signal model of UWB backscattering system is significantly different from that of conventional UWB system. Furthermore, the aforementioned TOA estimators do not include clutter in their signal models during the derivation processes and hence they lack of proven clutter suppression capability. Consequently, those TOA estimators cannot be directly applied to UWB backscattering RFID s
According to the above discussion, the peculiar transmission and modulation characteristics of UWB backscattering system together with the clutter suppression requirement call for a dedicated treatment of the derivation for TOA estimator. With these requirements in mind, we derive a novel NDA LS TOA estimator for the UWB backscattering RFID system with antipodal 2PAM based on the tag structure proposed in [9]. The proposed estimator is able to recover the TOA information even when the clocks of readers and tags are asynchronous. By examining the properties of the derived estimator, we find that it has inherent immunity to clutter for arbitrary data sequence which is highly desirable for backscattering RFID system. Unlike the aforementioned emptyroom and frametoframe techniques, such immunity holds regardless of environment changes and does not matter if the tags move or keep stationary. Simulation results indicate that the estimator is robust in undersampling operation.
The rest of the paper is organized as follows. Section 2 describes the system model and signal model. Section 3 derives the LS estimator whose immunity to clutter is discussed in Section 4. Simulation results are presented in Section 5. Finally, a conclusion is drawn in Section 6.
2. System Model and Signal Model
As illustrated in Figure 1, the tag under consideration consists of an antenna, an antipodal 2PAM modulator and a bipolar sequence generator with output being or . The load condition of the tag antenna is determined by the 2PAM modulator which is controlled by the sequence generator. The 2PAM modulator retains the polarity of its input signal if +1 is generated by the sequence generator, and it inverts the polarity if 1 is generated. The tag antenna acts like a scatterer and its scattering mechanism can be classified as structural mode and antenna mode [33]. The structural mode scattering is solely determined by the physical properties of the antenna and is independent of the load condition of the antenna. Thus the signal caused by structure mode scattering cannot be modulated by the 2PAM modulator and hence does not carry any data information in the tag. In contrast, the signal incurred by the antenna mode scattering is received by the antenna and modulated with a bipolar sequence generated by spreading a ranging sequence with a pseudonoise (PN) code which has period , that is, for all . The PN code is unique for each tag and is used for multiuser interference suppression or spectrum smoothing. The ranging sequence is periodic with period . In the following discussion, the tag response refers to the signal caused by the antenna mode scattering while the unmodulated signal caused by the structural mode scattering is treated as part of the clutter.
where is the transmitted UWB pulse and is the frame duration.
where , and . The physical explanations of and are given as follows. As shown in Figures 2(b)–2(d), due to the asynchronism between the clocks of the reader and the tag, for , the channel response may be modulated by two consecutive data bits if the transition edge between the two data bits splits the channel response into two parts. Here accounts for the front part of the channel response whereas represents the tail. For , we have since the support region of does not overlap with the nonzero region of . Similarly, for , we have .
where is the response of the l th path in the clutter channel, is the propagation delay of that path, and is the number of clutter paths. has time support on , where is the maximum clutter spread.
where accounts for thermal noise and multiple access interference, the doublesided power spectral density of of , is the overall clutter waveform with defined in (9), is the uplink channel response to and can also be interpreted as the overall roundtrip tag response to the UWB pulse transmitted at , and have been defined in (8).
In the above derivation, we have assumed that the uplink channel has the same true maximum oneway channel delay spread as the downlink channel. We further assume that and so that the interframe interference (IFI) is avoided. As implied by (10), the tag response energy may vary for different frames since the data bits modulating and may be different. It is useful to define an averaged symbol energy to noise ratio , where is the energy per symbol of the tag response averaged over all possible data bits, that is, and is the expectation operator. Another parameter of interest is the signaltoclutter ratio , where and is the energy per symbol of the clutter.
3. LS TOA Estimation
In this section, a LS TOA estimator is derived based on which is the presumed maximum delay spread of the oneway channel. Generally, is not equal to its true value . As shown in Section 5, the discrepancy between and does affect the estimation accuracy and the optimum value of may be determined via simulation.
Here, and are data bits modulating the partial tag responses and , respectively.
where the norm operator computes the Euclidean distance of vector . The search for global minimum of a general multidimensional nonlinear function usually involves numerical searching using the genetic algorithms, grid searchers or other computational intensive algorithms. Fortunately, the nonlinear cost function (13) has some special properties that allow us to first reduce the variables set to be optimized from to , which drastically reduces the computational complexity. This reduction procedure can lead to the global minimum of the cost function. The simplified cost function with the reduced variable set is then minimized by searching over all possible discrete values of the variables to reach the global minimum. The details of minimization procedure are given as follows.
where .
where is a constant irrelevant to and .
where , , , and and are vectors with their respective k th elements being , , and , for all . Note that , and only depend on the observed samples, the ranging and the PN codes.
Equation (34) indicates that the final solution involves a minimum search procedure over a threedimensional space span by variables . The complexity of this searching procedure is proportional to the number of possible discrete values of , , and , that is, proportional to the maximum number of samples prior to the TOA sample , the number of symbols , and the number of frames per symbol . Reducing or can lower the computational complexity. As a tradeoff, the TOA estimation accuracy will decrease accordingly. However, the simulation results in Section 5 will reveal that the TOA performance is very robust to the reduction in and is reduced by only about 3 dB for halving . Reducing also reduces the computational complexity which causes insignificant variation of TOA estimation as shown in the simulation results in Section 5. Therefore, our scheme does not require large and should be minimized for TOA estimation during system design phase. This minimum value of should be determined by other aspects of system design such as spectrum smoothing or the number of users in the system, which is out of the scope of this paper. Hence, by carefully setting , and , satisfactory performance can be achieved with reasonable complexity.
4. Immunity of the Estimator to Clutter
Any TOA estimator for UWB backscattering RFID system should posses clutter suppression capability, especially in an environment with dense scatterers. In [9], for data communication applications, clutter is suppressed by choosing a data sequence with zero mean (or quasizero mean). Intuitively we would have expected that similar sequence selection should also be imposed to ensure that the estimator derived in Section 3 is immune to clutter. However, we are going to show that the derived estimator is indeed inherently immune to clutter for arbitrary data sequence.
By observing (36) and (37), it is found that all the terms with symbolic term have been completely cancelled out during the derivation process, leaving no clutter term in the final expression. This finding suggests that clutter does not have any effect on the value of decision function. Therefore, the decision function (27) is immune to the clutter.
The absence of clutter term in (38) implies that the decision function (33) is also irrelevant to the clutter.
Consequently, based on the above discussions, it can be concluded that the proposed estimator is immune to the clutter. This conclusion is also supported by the simulation results presented in the next section.
5. Simulation Results
where is the convolution operator, and is a oneway channel impulse response generated independently and randomly from the CM1 and CM2 channel models used for the tag response generation, is a Dirac delta function and is the propagation delay of the first path in the clutter. Assume that follows a uniform distribution, that is, . The length of clutter responses are truncated beyond which is slightly longer than the true maximum roundtrip delay spread of tag responses .
Summary of Adopted Symbols for Simulation.
Symbol  Definition  Value 

 True maximum length of oneway tag response 

 Maximum length of roundtrip clutter response 

 Maximum roundtrip propagation delay of tag response and clutter response 

 Code period of ranging sequence 

 The total number of channel realizations 

 Signal bandwidth 

 Tag processing delay 

Among the two estimators, the ED estimator with normalized threshold is more robust to the channel and noise variation since its adaptive threshold setting while the estimator derived based on GML criterion can potentially achieve higher resolution in practical system since it decouples the time resolution from the length of integration interval and the short interval is difficult to be implemented due to receiver hardware limitation [27].
To perform a fair comparison, we set , , and so that the time resolution of the three estimators, that is, the ED estimator with normalized threshold, the GML estimator, and the estimator presented in this paper, are the same. The rest of parameter settings are , , and . Figure 7 indicates that the estimator presented in this paper is superior to the two estimators presented in [24, 27]. The ED estimator with normalized threshold in [24] and the GML estimator in [27] have comparable accuracy and the performances of both of them degrade rapidly as SCR decreases. On the contrary, the accuracy of the estimator proposed in this paper remains constant as the SCR varies.
6. Conclusion
A novel NDA LS TOA estimator is proposed as a solution to overcome the undesired clutter signal for TOA estimation problem in UWB backscattering RFID system. Both theoretical study and simulation results indicate that the estimator is inherently immune to the clutter signal regardless of SNR variation. Simulation results also show that the performance of the estimator depends on the number of sampled symbols as well as the presumed channel delay spread setting. Also the study shows that the estimator is robust over undersampling operation.
Appendices
A.
Analogous to the proof for , we can also show that . Therefore, we can make the denotation where is a constant.
B
Equation (B.1) implies that only when , for all , the first equation (29) holds.
and the equality holds if , for all and is a constant. Note that is equivalent to since and can only be or . In conclusion, when , for all or , for all are fulfilled, at least one of (29) and (30) holds and the expression (27) becomes invalid.
Declarations
Acknowledgment
This paper was supported by the ASTAR SERC Project under Grant no. 0521210086.
Authors’ Affiliations
References
 Want R: An introduction to RFID technology. IEEE Pervasive Computing 2006, 5(1):2533. 10.1109/MPRV.2006.2View ArticleGoogle Scholar
 First report and order in the matter of revision of part 15 of commission's rules regarding ultrawideband transmission systems FCC; 2002.Google Scholar
 Win MZ, Scholtz RA: On the robustness of ultrawide bandwidth signals in dense multipath environments. IEEE Communications Letters 1998, 2(2):5153. 10.1109/4234.660801View ArticleGoogle Scholar
 Fontana RJ, Gunderson SJ: Ultrawideband precision asset location system. Proceedings of the Ultra Wideband Systems and Technologies (UWBST '02), May 2002, Baltimore, Md, USA 21: 147150.Google Scholar
 Gezici S, Tian Z, Giannakis GB, Kobayashi H, Molisch AF, Poor HV, Sahinoglu Z: Localization via ultrawideband radios: a look at positioning aspects of future sensor networks. IEEE Signal Processing Magazine 2005, 22(4):7084.View ArticleGoogle Scholar
 Dardari D, Chong CC, Win MZ: Thresholdbased timeofarrival estimators in UWB dense multipath channels. IEEE Transactions on Communications 2008, 56(8):13661378.View ArticleGoogle Scholar
 Dardari D, Chong CC, Win MZ: Analysis of thresholdbased TOA estimator in UWB channels. Proceedings of the European Signal Processing Conference (EUSIPCO '06), September 2006, Florence, ItalyGoogle Scholar
 Dardari D: Pseudorandom active UWB reflectors for accurate ranging. IEEE Communications Letters 2004, 8(10):608610. 10.1109/LCOMM.2004.836838View ArticleGoogle Scholar
 Dardari D, D'Errico R: Passive ultrawide bandwidth RFID. Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM '08), December 2008, New Orlean, Lo, USA 39473952.Google Scholar
 Hu S, Law CL, Dou W: Measurements of UWB antennas backscattering characteristics for RFID systems. Proceedings of the IEEE International Conference on UltraWideband (ICUWB '07), September 2007, Singapore 9499.Google Scholar
 Paolini E, Giorgetti A, Chiani M, Minutolo R, Montanari M: Localization capability of cooperative antiintruder radar systems. Eurasip Journal on Advances in Signal Processing 2008, 2008:14.Google Scholar
 Lottici V, D'Andrea A, Mengali U: Channel estimation for ultrawideband communications. IEEE Journal on Selected Areas in Communications 2002, 20(9):16381645. 10.1109/JSAC.2002.805053View ArticleGoogle Scholar
 Cramer RJM, Scholtz RA, Win MZ: Evaluation of an ultrawideband propagation channel. IEEE Transactions on Antennas and Propagation 2002, 50(5):561570. 10.1109/TAP.2002.1011221View ArticleGoogle Scholar
 Lee JY, Scholtz RA: Ranging in a dense multipath environment using an UWB radio link. IEEE Journal on Selected Areas in Communications 2002, 20(9):16771683. 10.1109/JSAC.2002.805060View ArticleGoogle Scholar
 Maravic I, Kusuma J, Vetterli M: Lowsampling rate UWB channel characterization and synchronization. Journal of Communications and Networks 2003, 5(4):319326.View ArticleGoogle Scholar
 Kusuma J, Maravic I, Vetterli M: Sampling with finite rate of innovation: channel and timing estimation for UWB and GPS. Proceedings of the International Conference on Communications (ICC '03), May 2003, Anchorage, AK, USA 5: 35403544.Google Scholar
 Li X, Pahlavan K: Superresolution TOA estimation with diversity for indoor geolocation. IEEE Transactions on Wireless Communications 2004, 3(1):224234. 10.1109/TWC.2003.819035View ArticleGoogle Scholar
 Falsi C, Dardari D, Mucchi L, Win MZ: Time of arrival estimation for UWB localizers in realistic environments. Eurasip Journal on Applied Signal Processing 2006, 2006:13.Google Scholar
 Song SH, Zhang QT: Multidimensional detector for UWB ranging systems in dense multipath environments. IEEE Transactions on Wireless Communications 2008, 7(1):175183.View ArticleGoogle Scholar
 Yang L, Tian Z, Giannakis GB: Nondata aided timing acquisition of ultrawideband transmissions using cyclostationarity. Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing, April 2003, Hong Kong 121124.Google Scholar
 Tian Z, Yang L, Giannakis GB: Symbol timing estimation in ultra wideband communications. Proceedings of the 36th Asilomar Conference on Signals Systems and Computers, November 2002, Pacific Grove, Calif, USA 19241928.Google Scholar
 Yang L, Giannakis GB: Timing ultrawideband signals with dirty templates. IEEE Transactions on Communications 2005, 53(11):19521963. 10.1109/TCOMM.2005.858663View ArticleGoogle Scholar
 Tian Z, Giannakis GB: A GLRT approach to dataaided timing acquisition in UWB radios—part I: algorithms. IEEE Transactions on Wireless Communications 2005, 4(6):29562967.View ArticleGoogle Scholar
 Guvenc I, Sahinoglu Z: Thresholdbased TOA estimation for impulse radio UWB systems. Proceedings of the IEEE International Conference on UltraWideband (ICUWB '05), 2005, Zurich, Switzerland 2005: 420425.Google Scholar
 Guvenc I, Sahinoglu Z: Threshold selection for UWB TOA estimation based on kurtosis analysis. IEEE Communications Letters 2005, 9(12):10251027. 10.1109/LCOMM.2005.1576576View ArticleGoogle Scholar
 Xu C, Law CL: Delaydependent threshold selection for UWB TOA estimation. IEEE Communications Letters 2008, 12(5):380382.View ArticleGoogle Scholar
 Rabbachin A, Oppermann I, Denis B: GML ToA estimation based on low complexity UWB energy detection. Proceedings of the IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '06), September 2006, Helsinki, Finland 15.Google Scholar
 Carbonelli C, Mengali U: Synchronization algorithms for UWB signals. IEEE Transactions on Communications 2006, 54(2):329338.View ArticleGoogle Scholar
 D'Amico AA, Mengali U, Taponecco L: Energybased TOA estimation. IEEE Transactions on Wireless Communications 2008, 7(3):838847.View ArticleGoogle Scholar
 Gezici S, Sahinoglu Z, Molisch AF, Kobayashi H, Poor HV: Twostep time of arrival estimation for pulsebased ultrawideband systems. Eurasip Journal on Advances in Signal Processing 2008, 2008:11.Google Scholar
 Ibrahim J, Buehrer RM: Twostage acquisition for UWB in dense multipath. IEEE Journal on Selected Areas in Communications 2006, 24(4 I):801807.View ArticleGoogle Scholar
 Dardari D, Conti A, Ferner U, Giorgetti A, Win MZ: Ranging with ultrawide bandwidth signals in multipath environments. Proceedings of the IEEE 2009, 97(2):404425.View ArticleGoogle Scholar
 Hu S, Chen H, et al.: Backscattering cross section of ultrawideband antennas. IEEE Antennas and Wireless Propagation Letters 2007, 6: 7073.View ArticleGoogle Scholar
 Boyd S, Vandenberghe L: Convex Optimization. Cambridge University Press, New York, NY, USA; 2004.View ArticleMATHGoogle Scholar
 Bonnans JF, et al.: Numerical Optimization—Theoretical and Practical Aspects. Springer, New York, NY, USA; 2006. Section EditionMATHGoogle Scholar
 Molisch AF, et al.: IEEE 802.15.4a channel model—final report. Document IEEE 802.1504066202004a, 2005Google Scholar
 Abramowitz M, Stegun IA: Handbook of Mathematical Function with Formulas, Graphs, and Mathematical Tables. Dover, New York, NY, USA; 1970.MATHGoogle Scholar
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