Direct path detection using multipath interference cancelation for communicationbased positioning system
 Jiaxin Yang^{1},
 Xianbin Wang^{1}Email author,
 Sung Ik Park^{2} and
 Heung Mook Kim^{2}
DOI: 10.1186/168761802012188
© Yang et al.; licensee Springer. 2012
Received: 25 November 2011
Accepted: 5 August 2012
Published: 30 August 2012
Abstract
Abstract
Recent development in wireless communicationbased positioning technology brings significant challenge of detecting the weak signal component arriving from the direct propagation path for timeofarrival (TOA)based approach. Due to the common obstruction of the direct propagation path in dense multipath environments, identification of the weak direct path in these environments can be very difficult via the classical correlationbased estimator in the presence of interference from significant later arriving multipath components. A new direct path detection scheme using the multipath interference cancelation is presented in this article. For data communication purpose, an iterative estimator with improved accuracy for joint channel and data estimation is first developed; based on which, the interference of multipath components are reconstructed and subtracted from the original received signal. With the aid of the detected data, an enhanced preamble is formulated. The accuracy of direct path detection is substantially improved by using the correlation between the multipath interferencesuppressed signal and the enhanced preamble. Semianalytical expression of the performance of the iterative estimator is derived. The analysis enables the system to determine an automatic stopping criterion to reduce the computational complexity of the iterative process. The performance of the direct path detection is also analyzed in terms of the signaltointerferencenoise ratio (SINR) and compared with that of the conventional approach. Computer simulation results show the superiority of the proposed direct path detection. The accuracy of the positioning system using the proposed method is also evaluated in dense multipath environments.
Keywords
Direct path Later path Multipath interference cancelation Timeofarrival Dense multipath environment Orthogonal frequencydivision multiplexingIntroduction
Positioning techniques using wireless communication signals have attracted considerable attention, especially in dense multipath environments where the Global Positioning System (GPS) is often unreliable[1, 2]. Potential applications include navigation, locationaware services, and asset tracking[3, 4]. A few positioning system using data communication systems, e.g., cellular network[5, 6], wireless local area network (WLAN)[7], digital television (DTV)[8], ultrawide bandwidth (UWB)[2, 9–11], and wireless sensor networks[12, 13] have been investigated in the literature. In these systems, timeofarrival (TOA)based approach is commonly preferred where the timing of the signal arriving from the direct propagation path is estimated and used for further location estimation[14, 15]. However, significant challenges arise in dense multipath environments, where the direct signal propagation path is often blocked by various obstacles such as buildings, pedestrians and slowlymoving vehicles, making the strength of the direct path significantly weaker than those of later paths (LPs) from scattering, reflection or refraction.
TOA estimation can be achieved by the classical correlationbased estimator, where the correlation is performed between a signal template^{a} and its received version, and the timing of the first detected peak of the correlation output is considered as the TOA[9, 16]. However, this method has limiting factors for positioning system using communication signals. The training sequence, designed for data communications, has limited duration in order to improve the transmission efficiency and therefore, the correlation peak of the first path is not distinct. Furthermore, due to the multipath propagation effect, the desired signal component of the first path is interfered by the superposition of the signal components of the LPs. In this case, the correlation output can exhibit adjacent peaks with similar or larger magnitude due to the large multipath interference, leading to ambiguity in the selection of the correct peak corresponding to the first path. Therefore, it is very difficult to reliably detect the weak direct path from the wireless signals consisting of strong multiple components and thus, large positioning error is inevitable when the direct path is erroneously identified.
To facilitate the TOA estimation with higher accuracy, superresolution schemes that jointly estimate the amplitude and the relative delay of each path were proposed in[17–19]. Despite the extremely high computational complexity the methods entail, the results show that these estimators do not always lead to good TOA estimation. For real applications with complexity constraints, several simple techniques have been proposed to detect the first path. Energy detectionbased method was proposed in[20–23]. However, the energy detector can perform poorly when the strength of the first path is not the strongest one. Meanwhile, thresholdbased approach to detect the first path by comparing the correlation output with a particular threshold[20, 24–29] are gaining interests due to their potential for complete analog implementation, and therefore, particularly attractive to low cost batterypowered devices, e.g., mobile receivers. However, the thresholdbased correlation estimator with the solutions to the aforementioned practical challenges in dense multipath environments remains large space undisclosed.
We introduced the idea of multipath interference cancelation in[30]; however, with a simple system model and performance evaluation under static channel. Furthermore, the theoretical performance and the practical constraints, e.g., complexity issues, have not been considered. In this article, we propose a direct path detection scheme using multipath interference cancelation for accurate TOA estimation in dense multipath environments. We consider an Orthogonal FrequencyDivision Multiplexing (OFDM)based communication system, which is widely adopted in various broadband wireless applications[31] and demonstrate the feasibility of the proposed direct path detection method in communicationbased positioning systems. For data communication purpose, an iterative estimator for joint channel estimation and data detection with progressively improved accuracy is first proposed to achieve better performance of data communication. Based on the output of the iterative estimator, we propose to reconstruct and mitigate the multipath components from the received signals. Dataaided method is also used to construct an enhanced preamble. The new direct path detection is therefore based on the crosscorrelation between the multipath interferencesuppressed signal and the enhanced preamble. Considering the computational burden for the mobile receivers, the characteristics of the iterative estimator is studied by deriving a semianalytical expression of the variance of the estimation error and the convergence conditions. An automatic stopping criterion is further developed to avoid the unnecessary computational complexity and allow a tradeoff between the performance degradation and computational burden. The performance of the direct path detection is analyzed by deriving the signaltonoiseinterference ratio (SINR)^{b}. Monte Carlo simulations are carried out to evaluate and verify the performance and effectiveness of different modules as well as the overall method.
The rest of the article is organized as follows. The model of the OFDMbased communication system is presented in Section ‘OFDMbased communication system with positioning capability’. The challenges of the classical correlationbased estimator for TOA estimation are also addressed in Section ‘OFDMbased communication system with positioning capability’. In Section ‘Proposed direct path detection scheme’, a new direct path detection scheme using the multipath interference cancelation and dataaided techniques are proposed. Performance analysis is presented in Section ‘Performance analysis of the proposed direct path detection method’ and demonstrated by Monte Carlo simulations in Section ‘Simulation results and discussions’. Finally, we conclude the article in Section ‘Conclusion’.
Notations: Throughout the article, if no special note is given, we denote vectors and matrices with letters in bold fonts and scalars with nonbold forms. For any variable X, we denote its corresponding estimated version by$\widehat{X}$ and the corresponding estimation error by$\mathrm{\Delta X}\triangleq X\widehat{X}$. Superscript T denotes transpose and superscript H denotes the conjugate transpose. tr(·) denotes the trace of a matrix. I denotes the identity matrix. ∥·∥ denotes the Frobenius norm of a matrix.
OFDMbased communication system with positioning capability
System model
Classical correlationbased TOA estimator
The conventional approach, which is based on the crosscorrelation between the received signal and the local signal template, is widely used in communications as well as positioning systems. The time index of the earliest detected peak on the correlation output can then be converted to the corresponding arrival time of the signal traveling along the direct propagation path. However, our approach aims for positioning with traditional communication systems where the existing signal structures are not originally designed for good positioning capabilities, e.g., short preamble signal, and therefore, the first path can have a weak correlation peak and it is also severely distorted by the interference from later arriving multipath components in dense multipath environments. We now analyze the performance of this method.
 1.
msequence exhibits excellent correlation properties [32] such that the interference components from the LPs in (5) have been minimized by the preamble itself. In this case, the performance gain of the proposed direct path detection over the classical estimator will serve as a lower bound for other cases. Therefore, improved performance gain can further be expected when other types of preambles are used.
 2.
Unlike some complexvalued sequences, the msequence has low hardware implementation complexity which has been widely employed in various research works and real applications, e.g., synchronization in OFDM systems [33, 34].
The SINR can be verified by noting that {h_{ l }} are independent, {a_{ n }} are known numbers and {w_{ n }} are also independent. In wireless environments such as indoor or dense commercial areas, the strength of the direct path is often significantly weaker than the LPs due to the obstructions, e.g.,$\left(\right)close="">{\sigma}_{0}^{2}\ll \sum _{l=1}^{L1}{\sigma}_{l}^{2}$. Therefore, interference from LPs’ components shown in (6) can have large negative impact on the SINR. It is straightforward to see that the duration of the preamble signal p and the interference from the LPs are two main challenges in the detection of the direct path. However, in data communication systems, the preamble signal is used for initial access, synchronization and channel estimation. To optimize the spectral efficiency in cellular networks, the overhead of such training has to be as low as possible to reduce the corresponding redundancy as long as the quality of service requirements are achieved. On the contrary, for positioning systems it is desired to have as long preamble as possible to provide large correlation gain for good timing estimation. To address the aforementioned challenges, we propose a new direct path detection scheme based on multipath interference cancelation and dataaided method in the following sections.
Proposed direct path detection scheme
Iterative estimator for joint channel estimation and data detection
However, the accuracy of LS channel estimation (and hence data detection) is usually not good enough. As our proposed multipath interference cancelation approach relies on the performance of the channel estimator, the improvement given by the proposed approach can be very limited when the LS estimator is used. We therefore propose an iterative estimator to provide accurate channel and data estimation which is of significant importance for further multipath interference cancelation purpose.
The basic idea of the proposed iterative estimator is to utilize data decision feedback to virtually extend the duration of the original preamble signal to refine the initial channel estimation. We want to use both P and the unknown X_{ M } in (2) instead of only P to improve the accuracy of channel estimation. An approximation of X_{ M } can be first obtained from the tentative demodulated OFDM data based on the initial channel estimation given in (7). However, this approximation is not reliable at the beginning and can be gradually improved when channel estimation improves. Hence, an iterative estimator is needed to progressively provide more accurate channel estimation. Consequently, the accuracy of both data and channel will be enhanced simultaneously as the process is iterated.
The proposed iterative estimator is described as follows:
It should be mentioned that the accuracy of the initial estimation is limited by the length of the preamble. Therefore, the performance of multipath interference cancelation will be dramatically degraded if a short preamble is used.
where we have defined$\mathbf{y}\triangleq {\left[{\mathbf{y}}_{p}^{T}\phantom{\rule{1em}{0ex}}\phantom{\rule{1em}{0ex}}{\mathbf{y}}_{d}^{T}\right]}^{T}$.
Set iteration index i = i + 1. The updated channel estimate is subsequently used for the next round of data demodulation. As the above process is iterated, the channel and data estimation are progressively enhanced. Step 2 is repeated until the automatic stopping criterion is fulfilled. The characteristics of the iterative estimator and the automatic stopping criterion will be derived in the subsequent analysis.
Automatic stopping criterion design
Due to the limited battery life of the mobile devices, an automatic stopping criterion is proposed to terminate the iterative process as early as possible to avoid unnecessary computations while the userspecified requirement is achieved.
 1.The iterative estimator with data decision feedback should only be used along with the preamble if the resulting variance of channel estimation error is smaller than that obtained during the initial estimation. Based on (9) and (22), it is easy to check that P _{e max}, the maximum P _{ e }, that assure the performance improvement when the iterative estimator is used, can be determined by$\frac{4\stackrel{2}{\text{sin}}\left(\Pi /M\right){P}_{e}}{p+N}+\frac{1}{p+N}{\sigma}_{n}^{2}\le \frac{1}{p}{\sigma}_{n}^{2},$(23)which results in${P}_{e\phantom{\rule{1em}{0ex}}max}=\frac{N{\sigma}_{n}^{2}}{4\stackrel{2}{\text{sin}}\left(\Pi /M\right)p}.$
 2.It can also be seen from (22) that P _{ e } is a function of the variance of the channel estimation error at the symbol detector, and can be given by${P}_{e}=f\left({\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\right),$(24)where the function f can be determined using computer simulations. f(x) is a monotonous increasing function of x within a close interval$\left[0,{\sigma}_{\mathit{\Delta}\mathbf{h}\phantom{\rule{1em}{0ex}}max}^{2}\right]$. Therefore, at the i th iteration,$\left(\right)close="">{P}_{e}^{\left(i\right)}$ can be given by${P}_{e}^{\left(i\right)}\approx f\left({D}_{0}+{D}_{1}{P}_{e}^{(i1)}\right),$(25)where$\begin{array}{ll}{D}_{0}& =\frac{1}{p+N}{\sigma}_{n}^{2}\phantom{\rule{2em}{0ex}}\\ {D}_{1}& =\frac{4\stackrel{2}{\text{sin}}\left(\Pi /M\right)}{p+N}.\phantom{\rule{2em}{0ex}}\end{array}$For the convergence problem, a reasonably good initial channel estimation where only the preamble is used is necessary to guarantee the convergence of the iterative estimator described in (25). Although it is very difficult to derive the analytical results of the iterative process, e.g., estimation error and convergence. We therefore derive a semianalytical result of the estimation error in (21) and based on which, we can also derive the initial conditions on the channel estimation and SER for the convergence purpose. These conditions are very important and can be used to evaluate the convergence of a given communication system when particular system configuration such as preamble structure, channel estimation algorithm and modulation scheme is predefined. In the following, we denote the variance of the channel estimation error of the initialization stage by$\left(\right)close="">{\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\left(0\right)$ and it must satisfy the following conditions:

$\left(\right)close="">{\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\left(0\right)$ is located in the interval$\left[0,{\sigma}_{\mathit{\Delta}\mathbf{h}\phantom{\rule{1em}{0ex}}max}^{2}\right]$, namely${\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\left(0\right)<{\sigma}_{\mathit{\Delta}\mathbf{h}\phantom{\rule{1em}{0ex}}max}^{2}.$(26)
This condition ensures a reasonably good initialization performance of the iterative estimator.

The variance of the channel estimation error should decrease with an increase in the number of iterations, namely$f\left({\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\left(0\right)\right)<\frac{{\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\left(0\right){D}_{0}}{{D}_{1}}.$(27)
This condition ensures that the iterative estimator does not diverge.

 3.Assuming the convergence of the iterative estimator in reasonable conditions, (22) implies that${\sigma}_{\mathit{\Delta}\mathbf{h}}^{2}\to \frac{{\sigma}_{n}^{2}}{p+N},$(28)
almost surely, as P_{ e } → 0. It should be mentioned that this bias cannot be removed when the length of the preamble and the OFDM data symbol is finite and it serves as a lower bound of the iterative estimator. In fact, P_{ e } is dependent on SNR and therefore this bias vanishes as SNR→ ∞.
where ϵ is a userdefined threshold factor depending on the tolerable performance degradation of the receiver. As a small ϵ that results in less performance degradation is often associated with large number of iterations and vice versa. Therefore, we simulate the performance of the iterative estimator with different threshold factors and it will be discussed in Section ‘Conclusion’. In realtime application, a tuneable threshold selection device can be equipped such that the user is flexible to adjust the threshold according to its required performance and power condition.
Proposed direct path detection method
Given the channel estimation results from the iterative estimator, the significant paths which introduce dominant interference to the direct path detection can be determined based on the estimated channel. We use a thresholdbased method to select the significant paths. Since the practical multipath channels often show some level of sparsity, where very limited channel paths carry significant energy. Usually the total AWGN perturbation from those nonsignificant paths (zero paths) is much higher than the channel energy carried by them. Therefore, choosing a relative high threshold can successfully reject those nonsignificant paths while detecting most of the significant paths.
where${\widehat{\mathbf{y}}}_{p}$ and${\widehat{\mathbf{y}}}_{d}$ denote the interferencesuppressed signal corresponding to the preamble and OFDM data, respectively.
This correlation output can provide significantly higher peak gain due to the use of the virtually extended signal template which consists of the original template and a “dirty template” from data detection results. Furthermore, the impact of the overlay multipath signal components are mitigated.
The earliest correlation peak that exceeds a particular threshold can be considered as the direct propagation path and its timing index is straightforward to be converted to the TOA estimation. Details of the selection of the threshold can be found in[24].
Remark . Note the above method performs well in dense multipath environments, especially in nonlineofsight conditions[2, 38–41], where the strength of direct path is significantly lower than the LPs. However, in the presence of lineofsight propagation (strong direct path), the strength of the direct path can be strong w.r.t. the LPs. In this case, the signal component of the direct path may also be mitigated during the multipath interference cancelation stage. We therefore propose the following algorithm to adapt the proposed method to the dynamic wireless propagation environments, ranging from nonlineofsight condition to lineofsight condition.
Algorithm 1 Proposed direct path detection algorithm for dynamic propagation environments
Input: Received signals y_{ p }and y_{ d }.
Output: Time index of the direct path m_{0}.
 1.
Perform the iterative estimator using (7) to (15), and obtain the estimated channel $\widehat{{\mathbf{h}}^{\prime}}$ and data $\widehat{\mathbf{x}}$;
 2.Select the significant paths based on $\widehat{{\mathbf{h}}^{\prime}}$ according to the proposed direct path detection method;
 2.1
Detect the strongest path from the estimated channel using (31) and determine the threshold factor η according to the method in[37];
 2.2
Select the significant paths using (32);
 2.3
Record the time index of the first selected significant path, and denote it by k=m_{0};
 2.1
 3.Perform multipath interference cancelation and direct path detection using (31) to (36);
 3.1
Mitigate multipath interference using (33);
 3.2
Adopt dataaided method in (35);
 3.3
Detect the direct path based on (36), and record the time index of the detected peak$\left(\right)close="">k={m}_{0}^{\prime}$;
 3.1
if$\left(\right)close="">{m}_{0}^{{\phantom{\rule{0.1em}{0ex}}}^{\prime}}=\varnothing $then // No peak selected afterinterference cancelation
k = m_{0};
else
if$\left(\right)close="">{m}_{0}^{{\phantom{\rule{0.1em}{0ex}}}^{\prime}}{m}_{0}$then // A peak prior to m_{0} isdetected after interferencecancelation
$\left(\right)close="">k={m}_{0}^{{\phantom{\rule{0.1em}{0ex}}}^{\prime}}$;
else
k = m_{0};
end
end
Performance analysis of the proposed direct path detection method
To show the superiority of the proposed approach, we evaluate the SINR of the correlation output in (36) of the proposed method and compare it with (6).
The subsequent analysis are based on the following conditions:

The OFDM timedomain data {x_{ n }} and the regenerated data$\left\{{\widehat{x}}_{n}\right\}$ are mutually independent for different n and therefore it is also easy to show that$\mathrm{E}\left[\Delta {x}_{m}{\widehat{x}}_{n}^{\ast}\right]=0$ and$\mathrm{E}\left[\Delta {x}_{m}\Delta {x}_{n}^{\ast}\right]=0$ if m ≠ n;

The channel taps {h_{ l }} are independent complex Gaussian random variables with zero mean and variance$\left(\right)close="">{\sigma}_{l}^{2}$ and mutually independent of the channel estimation errors {Δ h_{ l }}. Also,${\sum}_{l=0}^{L1}{\sigma}_{l}^{2}=1$;

For analytical simplicity, we assume that the LPs mitigated by the proposed method carry most of channel energy such that$\sum _{l\in {R}_{S}}{\sigma}_{l}^{2}\approx 1$.
Since we have N ≪ p, significant SINR gain can be expected. This conclusion will be further verified through computer simulations in the following section.
Simulation results and discussions
Average power delay profiles of multipath channels
Normalized path delay  Channel I  Channel II  Channel III 

l)24  Average power (dB)  
0  −21.4  −12.8  −21.4 
1  −1.7  −1.9  −0.032 
2  −5.1  −5.3  0 
3  −20.5  −20.7  0 
7  −24.1  −24.3  0 
Performance of the joint iterative estimator
Impact of the automatic stopping criterion design
Performance of the proposed direct path detection method
Performance of TOAbased positioning system using the proposed method
Conclusion
A new direct path detection method using multipath interference cancelation scheme for TOA estimation is proposed for wireless communicationbased positioning systems. Based on the channel estimation and data detection results provided by the proposed iterative estimator, the interference from later arriving multipath components is reconstructed and removed from the original received signal. Performance of the proposed algorithm is evaluated through mathematical analysis and computer simulations. It is shown that the proposed algorithm is capable of improving the performance of direct path detection substantially with low complexity in dense multipath environments.
Endnotes
^{a}In data communication systems, the signal template can be a training sequence which is originally designed for multiple access, synchronization or channel estimation purposes.^{b}In[24], the authors demonstrated the error probability of the direct path detection is a monotonously decreasing function of the SINR. Therefore, to be more intuitive, we characterize the performance of the proposed direct path detection in terms of the SINR.
Appendix
Derivation of equation (21)
Abbreviations
 GPS:

Global Positioning System
 WLAN:

wireless local area network
 DTV:

digital television
 UWB:

ultrawide bandwidth
 TOA:

timeofarrival
 LP:

later path
 OFDM:

Orthogonal FrequencyDivision Multiplexing
 SINR:

signaltonoiseinterference ratio
 GI:

guard interval
 ISI:

intersymbol interference
 AWGN:

additive white Gaussian noise
 LS:

Least Square
 EPA:

Extended Pedestrian A
 MSE:

mean square error
 SER:

symbol error rate.
Declarations
Acknowledgements
This study was supported in part by Western Innovation Fund and Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant R4122A02 and ETRI Project KI002167.
Authors’ Affiliations
References
 Kaplan ED: Understanding GPS: Principles and Applications. Artech House, Norwood, Mass, USA; 1996.Google Scholar
 Conti A, Ferner U, Giorgetti A, Win MZ, D Dardari: Ranging with ultrawide bandwidth signals in multipath environments. Proc. IEEE 2009, 97(2):404426.View ArticleGoogle Scholar
 FCC: Revision of the commission’s rules to ensure compatibility with enhanced 911 emergency calling system. ET Docket No 1996, 94102.Google Scholar
 Sayed AH, Tarighat A, Khajehnouri N: Networkbased wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE Signal Process. Mag 2005, 22(4):2440.View ArticleGoogle Scholar
 Cong L, Zhuang W: Hybrid TDOA/AOA mobile user location for wideband CDMA cellular systems. IEEE Trans. Wirel. Commun 2002, 1(3):439447. 10.1109/TWC.2002.800542View ArticleGoogle Scholar
 Caffery JJ, Stüber GL: Subscriber location in CDMA cellular networks. IEEE Trans. Veh. Technol 1998, 47(2):406416. 10.1109/25.669079View ArticleGoogle Scholar
 Prasithsangaree P, Krishnamurthy P, Chrysanthis PK: On indoor position location with wireless LANs. In Proc. 13th IEEE Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC’02), vol. 2,. Lisboa, Portugal; 2002:pp. 720724.View ArticleGoogle Scholar
 Wang X, Wu Y, Chouinard JY: A new position location system using DTV transmitter identification watermark signals. EURASIP J. Appl. Signal Process 2006, Article ID 42737, 2006: 111.Google Scholar
 Gezici S, Poor HV: Position estimation via ultrawideband signals. Proc. IEEE 2009, 97(2):386403.View ArticleGoogle Scholar
 Shen Y, Win MZ: Fundamental limits of wideband localization—part i: a general framework. IEEE Trans. Inf. Theory 2010, 56(10):49564980.MathSciNetView ArticleGoogle Scholar
 Shen Y, Wymeersch H, Win MZ: Fundamental limits of wideband localization – part ii: Cooperative networks. IEEE Trans. Inf. Theory 2010, 56(10):49815000.MathSciNetView ArticleGoogle Scholar
 Win MZ, Conti A, Mazuelas S, Shen Y, Gifford WM, Dardari D, Chiani M: Network localization and navigation via cooperation. IEEE Commun. Mag 2011, 49(5):5662.View ArticleGoogle Scholar
 Patawari N, Ash JN, Kyperountas S, Hero AO, Moses RL: Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process. Mag 2005, 22(4):5469.View ArticleGoogle Scholar
 Qi Y, Kobayashi H, Suda H: On timeofarrival positioning in a multipath environment. IEEE Trans. Veh. Technol 2006, 55(5):15161526. 10.1109/TVT.2006.878566View ArticleGoogle Scholar
 Lee JY, Scholtz AR: Ranging in a dense multipath environment using an UWB radio link. IEEE J. Sel. Areas Commun 2002, 20(9):16771683. 10.1109/JSAC.2002.805060View ArticleGoogle Scholar
 Van Trees HL: Detection, Estimation, and Modulation Theory. John Wiley & Sons, Inc., New York, USA; 1968.Google Scholar
 Li X, Pahlavan K: Superresolution TOA estimation with diversity for indoor geolocation. IEEE Trans. Wirel. Commun 2004, 3(1):224234. 10.1109/TWC.2003.819035View ArticleGoogle Scholar
 Manabe T, Takai H: Superresolution of multipath delay profiles measured by PN correlation method. IEEE Trans. Antennas Propagat 1992, 40(5):500509. 10.1109/8.142624View ArticleGoogle Scholar
 Bouchereau F, Brady D, Lanzl C: Multipath delay estimation using a superresolution PNcorrelation method. IEEE Trans. Signal Process 2001, 49(5):938949. 10.1109/78.917798View ArticleGoogle Scholar
 Guvenc I, Sahinoglu Z: Thresholdbased TOA estimation for impulse radio UWB systems. In Proc. IEEE Int. Conf. UltraWideband, 2005 (ICU’05). Zurich, Switzerland; 2005:pp. 420425.Google Scholar
 Cheong P, Rabbachin A, Montillet J, Yu K, Oppermann I: Synchronization, toa and position estimation for lowcomplexity LDR UWB devices. In Proc. IEEE Int. Conf. UltraWideband, 2005 (ICU’05). Zurich, Switzerland; 2005:pp. 480484.View ArticleGoogle Scholar
 D’Amico AA, Mengali U, Taponecco L: Energybased TOA estimation. IEEE Trans. Wirel. Commun 2008, 7(3):838847.View ArticleGoogle Scholar
 Guvenc I, Sahinoglu Z: TOA estimation with different IRUWB transceiver types. In Proc. IEEE Int. Conf. UltraWideband, 2005 (ICU’05). Zurich, Switzerland; 2005:pp. 426431.View ArticleGoogle Scholar
 Dardari D, Chong CC, Win MZ: Thresholdbased timeofarrival estimators in UWB dense multipath channels. IEEE Trans. Commun 2008, 56(8):13661378.View ArticleGoogle Scholar
 Alavi B, Pahlavan K: Modeling of the TOAbased distance measurement error using UWB indoor radio measurements. IEEE Commun. Lett 2006, 10(4):275277. 10.1109/LCOMM.2006.1613745View ArticleGoogle Scholar
 Falsi C, Dardari D, Mucchi L, Win MZ: Time of arrival estimation for UWB localizers in realistic environments. EURASIP J. Appl. Signal Process. vol. 2006 (2006, Article ID 32082) pp. 1–13Google Scholar
 Xu C, Law CL: Delaydependent threshold selection for UWB TOA estimation. IEEE Commun. Lett 2008, 12(5):380382.View ArticleGoogle Scholar
 Gezici S, Sahinoglu Z, Molisch AF, Kobayashi H, Poor HV: Twostep time of arrival estimation for pulsebased ultrawideband systems. EURASIP J. Adv. Signal Process. vol. 2008 (2008, Article ID 529134) pp. 1–11View ArticleGoogle Scholar
 Sahinoglu Z, Guvenc I: Multiuser interference mitigation in noncoherent UWB ranging via nonlinear filtering. EURASIP J. Wirel. Commun. Network 2006, 2006: 110. (Article ID 56849)View ArticleGoogle Scholar
 Yang J, Wang X, Park SI, Kim HM: A novel first arriving path detection algorithm using multipath interference cancellation in indoor environments. In Proc. IEEE 72nd Veh. Technol. Conf. (VTC’10 Fall). Ottawa, Canada; 2010:pp. 15.Google Scholar
 Hwang T, Yang C, Wu G, Li S, Li GY: OFDM and its wireless applications: a survey. IEEE Trans. Veh. Technol 2009, 58(4):16731694.View ArticleGoogle Scholar
 Sarwate DV, Pursley B: Cross correlation properties of pseudorandom and realted sequences. Proc. IEEE 1980, 68: 593619.View ArticleGoogle Scholar
 Tufvesson F, Edfors O, Faulkner M: Time and frequency synchronization for OFDM using PNsequence preambles. In Proc. IEEE 50th Veh. Technol. Conf. (VTC ’99Fall), vol. 4. Amsterdam, Netherlands; 1999:pp. 22032207.Google Scholar
 Puska H, Saarnisaari H: Matched filter time and frequency synchronization method for OFDM systems using PNsequence preambles. In Proc. 18th IEEE Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC ’07). Athens, Greece; Sep. 2007:pp. 15.Google Scholar
 Beek JJ, Edfors O, Sandell M, Wilson SK, Börjesson PO: On channel estimation in OFDM systems”. In Proc. IEEE 45th Veh. Technol. Conf. (VTC ’95), vol. 2. Chicago, IL; 1995:pp. 815819.Google Scholar
 Li H, Betz SM, Poor HV: Performance analysis of iterative channel estimation and multiuser detection in multipath DSCDMA channels. IEEE Trans. Signal Process 2007, 55(5):19811993.MathSciNetView ArticleGoogle Scholar
 Minn H, Bhargava VK, Letaif KB: A robust timing and frequency synchronization and channel estimation for OFDM. IEEE Trans. Commun 2003, 2(4):822839.Google Scholar
 Guvenc I, Chong CC, Watanabe F: NLOS identification and mitigation for UWB localization systems. In Proc. IEEE Wireless Commun. Netw. Conf. 2007 (WCNC’07). Kowloon, HongKong; 2007:pp. 15731578.Google Scholar
 Wylie MP, Holtzman J: The nonline of sight problem in mobile location estimation. In Proc. 5th IEEE Conf. Universal Pers. Commun. 1996. Cambridge, MA; 1996:pp. 827831.Google Scholar
 Casas R, Marco A, Guerrero JJ, Falco J: Robust estimator for nonlineofsight error mitigation in indoor localization. EURASIP J. Appl. Signal Process. vol. 2006 (2006, Article ID 43429) pp. 1–8View ArticleGoogle Scholar
 Marano S, Gifford WM, Wymeersch H, Win MZ: NLOS identification and mitigation for localization based on UWB experimental data. IEEE J. Sel. Areas Commun 2010, 28(7):10261035.View ArticleGoogle Scholar
 Evolved universal terrestrial radio access (EUTRA);: user equipment (UE) radio tranmission and reception (Release 8), Technical Specification, 3GPP (TR 36.803),. Sophia Antipolis, France (2007)
 Molkdar D: Review on radio propagation into and within buildings. IEE Proc. H: Microwaves Antennas Propagat 1991, 138(1):6173. 10.1049/iph2.1991.0011Google Scholar
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