Positioning Performance of LTE Signals in Rician Fading Environments Exploiting Antenna Motion

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1 Positioning Performance of LTE Signals in Rician Fading Environments Exploiting Antenna Motion Kimia Shamaei, Joshua J. Morales, and Zaher M. Kassas University of California, Riverside BIOGRAPHIES Kimia Shamaei is a Ph.D. candidate at the University of California, Riverside and a member of the Autonomous Systems Perception, Intelligence, and Navigation (ASPIN) Laboratory. She received her B.S. and M.S. in Electrical Engineering from the University of Tehran. Her current research interests include analysis and modeling of signals of opportunity and software-defined radio. Joshua J. Morales is a Ph.D. candidate at the University of California, Riverside and a member of the ASPIN Laboratory. He received a B.S. in Electrical Engineering with High Honors from the University of California, Riverside. His research interests include estimation, navigation, autonomous vehicles, and intelligent transportation systems. Zaher (Zak) M. Kassas is an assistant professor at the University of California, Riverside and director of the AS- PIN Laboratory. He received a B.E. in Electrical Engineering from the Lebanese American University, an M.S. in Electrical and Computer Engineering from The Ohio State University, and an M.S.E. in Aerospace Engineering and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin. In 2018, he received the National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) award. His research interests include cyber-physical systems, estimation theory, navigation systems, autonomous vehicles, and intelligent transportation systems. ABSTRACT A navigation framework based on a multi-state constraint Kalman filter (MSCKF) is proposed to reduce the effect of time-correlated pseudorange measurement noise of cellular long-term evolution (LTE) signals. The proposed MSCKF navigation framework uses a tightly-coupled inertial navigation system (INS) aided by pseudoranges to multiple enodebs to estimate the position of the receiver along with the difference of the clock bias of the receiver and each of the enodebs. Simulation results with a Rician fading channel multipath environment are presented showing a reduction of 44.31% in the two-dimensional (2D) position root mean squared-error (RMSE) using the proposed approach compared to an extended Kalman filter (EKF) approach. Experimental results on a ground vehicle navigating in an urban environment are presented showing a 2D RMSE of 6.05 m over a trajectory of 1380 m using the proposed approach. The results show a reduction of 48% in the 2D position RMSE and 50% in the 3D position RMSE using the proposed framework compared to an EKF. I. INTRODUCTION Traditional approaches to overcome global positioning satellite system (GNSS) limitations focused on fusing GNSS receivers with sensors (e.g., gyroscope, accelerometer, compass, barometer, lidar, camera, etc.). Recent approaches aimed at exploiting ambient signals of opportunity (SOPs). SOPs are radio frequency (RF) signals which are not designed for navigation purposes, but are freely available and may be exploited for navigation and timing when GNSS signals become unusable [1 4]. Examples of SOPs are: (1) AM/FM radio [5, 6], (2) digital television (DTV) [7, 8], (3) wireless local area network (WLAN or Wi-Fi) [9, 10], and (4) cellular signals [11 16]. Cellular long-term evolution (LTE) signals are particularly attractive for navigation in deep urban canyons due to their desirable characteristics: abundance, large transmission bandwidth, high received power, frequency diversity, and geometric favorability of transmitter locations [17]. The literature on exploiting LTE signals for navigation have analyzed the achievable navigation accuracy with different types of LTE reference signals[18 20]. The navigation solution obtained from LTE signals was also evaluated Copyright c 2018 by K. Shamaei, J. Morales, and Z. M. Kassas Preprint of the 2018 ION GNSS Conference Miami, FL, September 24 28, 2018

2 experimentally [21, 22]. Several software-defined receivers (SDRs) have been proposed to track LTE signals, which have shown meter-level accuracy [12, 23, 24]. The effect of multipath on the achievable pseudorange accuracy have also been studied for different LTE reference signals and it was shown that the pseudorange error is reduced for signals with higher transmission bandwidth [25, 26]. Multipath is among the most significant challenges that must be addressed for RF-based navigation. Multipath introduces error in the estimated pseudoranges, where the magnitude of the error depends on the multipath condition, signal bandwidth, and sampling rate. The high transmission bandwidth of LTE signals (up to 20 MHz) could be used to resolve multipath. However, received LTE signals experience more multipath compared to GNSS signals, particularly for ground-based receivers in urban canyons, due to the low elevation angles at which signals are received. In a low dynamic environment, the multipath effect lasts over multiple epochs and the error due to multipath is time-correlated. Besides, the pseudorange error caused by multipath may affect multiple epochs due to the receiver s loop filter. Several signal processing-based methods have been proposed to remove the effect of multipath in GNSS signals including a multipath-estimating delay-locked loop (MEDLL) [27], a batch filter to estimate the multipath using a known antenna motion [28], and a technique to correct multipath errors using signal-to-noise ratio [29]. These approaches have either high computational cost or they require prior knowledge of the multipath condition. Another approach to reduce the effect of multipath is based on beamforming. Beamforming can be performed using an antenna array, which has a bulky structure. Alternatively, one can synthesize an antenna array by moving the antenna. In synthetic aperture navigation (SAN), the antenna movement can be uniform or arbitrary. In a uniform movement, computationally low-cost approaches (e.g., multiple signal classification (MUSIC) or estimation of signal parameters via rotational invariance techniques (ESPRIT)) can be used to estimate the direction-of-arrival (DOA) [30, 31], whereas computationally expensive algorithms (e.g., space-alternating generalized expectation-maximization (SAGE)) must be used to estimate the DOA in an arbitrary movement [32]. Uniform structures require a bulky hardware platform, and the performance of arbitrary structures depends on the accuracy of the antenna location, which depends on the accuracy of the inertial measurement unit (IMU). It has been shown that the antenna motion can also be used to decorrelate the error induced by multipath. Antenna motion was used in [33] to improve the detection performance and in [34] to reduce the carrier-phase error in carrier-phase differential GNSS (CDGNSS) positioning, where a first-order Gauss-Markov process was used to model the relative antenna position with respect to the reference antenna. In a Kalman filter, the measurement noise is assumed to be time-uncorrelated. A time-correlated measurement noise will induce an error in the navigation solution estimate. Since the dynamics of the errors due to multipath is unknown, whitening approaches cannot be used to decorrelate the measurement noise. This paper addresses this challenge by making two contributions. First, a navigation framework based on a multi-state constraint Kalman filter (MSCKF) is proposed. The MSCKF was first introduced in robotics literature [35]. In this framework, an IMU is used to capture the position of the antenna over a window of measurements. Unlike a traditional extended Kalman filter (EKF), which uses a single measurement epoch to update the state estimate, this paper employs an MSCKF to use a sliding window of measurement epochs along with the antenna motion to decorrelate the measurement noise and provide constraints on the position estimate. Moreover, the difference of the clock bias of the receiver and each of the LTE base stations (also known as evolved node Bs or enodebs) are estimated along with the position of the antenna since the enodebs clock biases are unknown to the receiver. Second, the results are evaluated with simulations and experiments. There are several factors that characterize the behavior of a wireless channel e.g., terrain features, relative speed of the transmitter and receiver, etc. Propagation mode including line-of-sight (LOS), reflections, diffractions, and scattering is among these factors to characterize a wireless channel. Two channel models were introduced to capture these factors namely Rician and Rayleigh fading channels [36]. A channel with LOS can be modeled with a Rician fading, while a channel with no LOS can be modeled with a Raleigh fading. The simulation environment assumes the receiver to have access to the LOS signal and the channel is modeled as a Rician fading channel. The pseudorange errors are simulated assuming a time-correlated Rician channel. The trade-off between the integration time and the achievable positioning accuracy is analyzed in the simulations. The experimental results show a reduction of 48% in the 2-dimensional (2D) position root mean squared-error (RMSE) and a reduction of 50% in the 3D position RMSE using the proposed approach compared to using a traditional EKF. The remainder of this paper is organized as follows. Section II presents the proposed navigation framework, which specifies the state, propagation, and update models. Section III shows simulation results comparing the proposed

3 method with an EKF approach. Section IV presents experimental results. Section V concludes the paper. II. NAVIGATION FRAMEWORK This section presents the MSCKF-based navigation framework. A. State Propagation and Augmentation The MSCKF state vector comprises (1) IMU states, (2) clock error states, and (3) a history of the receiver s past position and the difference of clock biases between the receiver and each of the enodebs. The IMU measurements are fed to an inertial navigation system (INS) as they become available, which propagates the receiver s position state. The clock error states are propagated at the same rate as the IMU states according to the standard model describing the time evolution of the clock bias and drift (double integrator driven by noise) [37,38], which is composed of the clock bias and drift. Once LTE pseudorange measurements become available, the current receiver s position and the difference of clock biases of the receiver and each of the enodebs are appended to the state vector and the pseudorange measurements are appended to the measurement vector. After N measurements have been obtained, the EKF undergoes a measurement update using all N measurements to impose constraints between all N appended receiver positions from which the pseudorange measurements were obtained. The vehicle s state vector x r is defined as x r [ x T IMU, xt clk, πt 1, πt 2, ] T, πt N where x IMU represents the IMU state vector, x clk is the clock state vector, and π i is composed of the receiver s position and the difference between the clock bias of the receiver and each of the enodebs at the i-th pseudorange measurement. The IMU state vector is given by [ ] T, x IMU IG q T, G r T IMU, G v T IMU, bt g, bt a where I G qt is the unit quaternion representing the rotation from a global frame G, such as an Earth-centered inertial (ECI) frame, to the IMU frame I; G r T IMU = [x IMU,y IMU,z IMU ] T and G v T IMU = G ṙ T IMU are the 3D position and velocity of the IMU in the global frame, respectively; and b T g and b T a are the gyroscope and accelerometer biases, respectively. Standard IMU state propagation model can be used to propagate the states of the IMU [38 41]. The clock bias state vector is defined as x clk [x (1) T (U) clk,, x whereu isthe totalnumberofenodebs; x (u) clk = [c δt(u),c δt (u) ]; δt (u) = δt r δt s (u) with δt r andδt s (u) representing the clock biases of the receiver and the u-th enodeb, respectively; δt (u) = δt (u) r δt s with δt (u) r and δt s representing the clock drifts of the receiver and the u-th enodeb, respectively. The vector π i is defined as clk [ ] π i G r T, x T T Ai c i, [ ] T where G r Ai = [x Ai,y Ai,z Ai ] T and x ci = c δt (1) i,,c δt (U) i are antenna s position in the global frame and the difference between the clock bias of the receiver and each of the enodebs, respectively, at the i-th pseudorange measurement. T ] T,

4 B. Measurement Update Once N pseudorange measurement epochs are appended the measurement vector, an EKF measurement update is performed. After each update, the states corresponding to the antenna s position and the difference between the clock bias of the receiver and each of the enodebs corresponding to N rem epochs are removed from the state vector. The pseudorange measurements corresponding to these states are also removed from the measurement vector and the filter returns to the state propagation stage. III. SIMULATION RESULTS To evaluate the proposed framework, a simulation environment was developed comprising a receiver navigating in an urban area (downtown Riverside, California) over a 6 km trajectory that includes straight segments and turns. The locations of the enodebs were simulated using real enodebs locations in that environment. The simulation environment showing the receiver s trajectory and the enodebs positions is shown in Fig. 1. The receiver s and enodebs clocks were simulated with a temperature-compensated crystal oscillator (TCXO) and an oven-controlled crystal oscillator (OCXO), respectively. 0 m 250 m Fig. 1. Simulated traversed trajectory and the positions of the LTE enodebs. Map data: Google Earth The IMU s rotational velocity and linear acceleration measurements were generated at T = 0.01 s. The IMU s measurement noise and time evolution of the IMU s biases are determined by the grade of the IMU. In this work, data for a consumer-grade IMU was generated. It is assumed that the LTE pseudoranges were estimated every LTE frame duration, which is T sub = 10 ms. A Rayleigh and a Rician fading model can be used to model the propagation mode of the wireless channel. In a Rayleigh fading, it is assumed that the signal does not have LOS. While in the Rician fading channel, it is assumed that the signal has LOS. In this section, the receiver is assumed to have access to LOS. Therefore, a Rician fading channel was used to model the channel impulse response (CIR). Moreover, to characterize the LOS and multipath signal power and delay profile, the CIR was simulated based on an extended vehicular A (EVA) channel model [42] and the multipath error affecting the pseudorange was simulated based on the model presented in [43, 44]. The simulated CIRs were assumed to be correlated with two different correlation coefficients = {0.8, 0.98}. For comparison purposes, Fig. 2 shows the simulated multipath for one of the enodebs over 10 s. The standard deviation of the generated multipath was 1.1 m. The measurement noise was assumed to be additive white Gaussian with a standard deviation obtained based on the carrier-to-noise ratio of the received signal for each enodeb [45]. The simulation was repeated for 20 different multipath and noise conditions. Fig. 3 shows the average of the 2D and 3D position RMSE overthe entire simulated trajectoryfor each run using the proposedmethod. The values of the 2D and 3D position RMSEs were obtained for different update time (i.e., N rem T sub ). The results were compared with an EKF, where the state update is done whenever a pseudorange measurement is available and no state augmentation is performed. For the sake of comparison, in the EKF approach, it is assumed that the pseudorange measurement is available every N rem T sub. The value of N was set to 100. The following conclusions can be drawn from the simulation results.

5 2D position RMSE [m] Amplitude of multipath error [m] 3D position RMSE [m] % = 0.98 % = 0.8 Time [s] Fig. 2. Example of the simulated multipath error for one enodeb over 10 s. MSCKF EKF % = 0.98 % = 0.8 Update time [s] Update time [s] Fig. 3. 2D and 3D position RMSE over the entire simulated trajectory for different update time. Remark 1 For both the MSCKF and EKF approaches, the 3D position RMSE is worse than the 2D RMSE, since the enodebs have approximately similar height and the geometric diversity in the vertical direction is poor. Remark 2 The proposed MSCKF approach outperforms the EKF approach. The reduction in the RMSE for = 0.98 is higher compared to = 0.8, which means that when the measurement noise is highly time-correlated, the proposed approach can significantly reduce the position estimation error. Remark 3 From several sets of simulations, it was concluded that a good rule of thumb for choosing N rem is such that N rem N/2. Such rule of thumb reduces the RMSE while maintaining a reasonablecomputational complexity (update time). Next, the 2D position RMSE was evaluated for different values of N. For this purpose, the value of N was selected fromthe set{0,25,50,100}and N rem wasset to N/2. Note that when N is zero, the MSCKF approachisequivalent to an EKF since no augmentation is performed. Fig. 4 shows the results for this simulation, which was obtained by averaging the obtained 2D position RMSE over 20 different simulated multipath and noise realizations. The results show that increasing N decreases the RMSE, especially for higher time-correlation in the measurement noise (i.e., = 0.98). However, increasing N increases the update time, which increases the computational burden. IV. EXPERIMENTAL RESULTS To evaluate the performance of the proposed approach, an experiment was performed in an urban area (downtown Riverside, California). This section describes the experimental hardware setup and presents the experimental results.

6 2D position RMSE [m] % = 0.98 % = 0.8 N Fig. 4. 2D position RMSE over the entire simulated trajectory for different values of N and for N rem = N/2 A. Experimental Hardware Setup A ground vehicle was equipped with two consumer-grade 800/1900 MHz cellular omnidirectional Laird antennas to receive the LTE signals at 739 MHz and 1955 MHz carrier frequencies, which were used by AT&T operator. A dualchannel National Instruments (NI) universal software radio peripheral (USRP)-2954R, driven by a GPS-disciplined oscillator (GPSDO) was used to simultaneously down-mix and synchronously sample LTE signals with 10 Msps. A laptop was used to store LTE samples for post-processing. A Septentrio AsteRx-i V, which is equipped with dual antenna multi-frequency GNSS receiver with real-time kinematic (RTK) and a Vectornav VN-100 micro electromachanical systems (MEMS) IMU, was used to estimate the position and orientation of the ground vehicle, which was used as the ground truth. The stored LTE samples were processed by the Multichannel Adaptive TRansceiver Information extractor (MATRIX) SDR developed by the Autonomous Systems Perception, Intelligence, and Navigation (ASPIN) Laboratory, producing pseudoranges to LTE enodebs in the environment. The proposed framework was used to estimate the receiver s position using the derived pseudoranges. The receiver was assumed to have access to GPS signals at its initial position. Therefore, the receiver was able to estimate the initial values of the its position and the difference of its clock bias with each of the enodebs, which makes the estimation problem observable [46]. Fig. 5 shows the experimental hardware setup. GPS antennas Cellular antennas Integrated GPS-IMU Fig. 5. Experimental hardware setup Storage USRP RIO Over the course of the experiment, the receiver was listening to 5 enodebs with the characteristics shown in Table I. The receiver traversed a trajectory of 1380 m over 190 s.

7 enodeb TABLE I ENodeBs Characteristics Carrier frequency (MHz) N Cell ID Bandwidth (MHz) The receiver s orientation, position, and velocity, and their covariances were initialized using the output of the AsteRx-i V s GNSS-INS. The gyroscope s and accelerometer s biases were initialized by taking the mean of 5 seconds of IMU data, when the receiver was stationary. The initial clock bias and drift uncertainties were set to 3 m 2 and 0.3 (m/s) 2, respectively. The measurement noise variance was determined empirically. B. Experimental Results Fig. 6(a) shows the estimated pseudoranges and their corresponding ranges for all enodebs. For comparison purposes, the initial value of the pseudoranges and ranges were subtracted from the pseudorange and range values over the entire trajectory. Therefore, the presented pseudoranges and ranges in Fig. 6(a) start from zero. Fig. 6(b) shows the empirical cumulative function (CDF) of the difference between the estimated pseudoranges and their corresponding actual ranges after removing the initial clock biases. These differences are due to the unmodeled effects, such as clock drifts, multipath, and measurement noise. Dashed: Pseudoranges Solid: Ranges enodeb 5 enodeb 4 enodeb 3 enodeb 2 enodeb 1 (a) Fig. 6. (a) Estimated pseudoranges and their corresponding ranges for each enodeb. The initial values were removed for comparison purposes. (b) Empirical CDF of the error between the pseudoranges and their corresponding ranges after removing the initial clock bias. Fig. 7 shows the 2D and 3D position RMSE for different values of N. In these results, it is assumed that = 0.98 and N rem = N/2. It can be seen that both the 2D and 3D position RMSE decreased by increasing N from 1 to 50. However, the payoff due to increasing N from 50 to 100 diminishes. Fig. 7 shows that the proposed MSCKF approach could reduce the 2D and 3D position RMSE by 5.55 m and m, respectively, compared to the EKF approach, where N is set to one. Fig. 8 compares the navigation solutions obtained by the proposed MSCKF approach and the EKF approach versus the ground truth. Table II summarizes the resulting 2D and 3D position RMSE. (b)

8 N Fig. 7. Experimental 2D and 3D RMSE for different values of N Trajectories: GPS LTE MSCKF LTE EKF 1000 m enodeb 2 enodeb 3 enodeb 1 enodeb 5 enodeb 4 Fig. 8. Experimental results showing the ground vehicle s ground truth trajectory (from a GNSS-IMU RTK system), the estimated trajectory with the proposed MSCKF and an EKF. The total traversed trajectory was 1380 m. Image: Google Earth. TABLE II 2D and 3D position RMSE Method 2D RMSE [m] 3D RMSE [m] MSCKF EKF V. CONCLUSION In this work, the effect of the time-correlated pseudorange error caused by multipath and the receiver s loop filter on the position estimation error was addressed. A navigation framework based on an MSCKF was proposed to reduce the positioning error in the presence of time-correlated error. Simulation results were presented to evaluate the

9 effect of the correlation on the position estimation error. The simulation results assumed a LOS channel with Rician fading. The simulation results showed a reduction of 44.31% in the 2D position RMSE using the proposed approach compared to the EKF. The experimental results showed a 48% reduction in the 2D position RMSE compared to the EKF approach. The total 2D position RMSE was 6.05 m for a ground vehicle navigating in an urban environment over a 1380 m trajectory. ACKNOWLEDGMENT This work was supported in part by the Office of Naval Research (ONR) under Grant N References [1] K. Fisher, The navigation potential of signals of opportunity-based time difference of arrival measurements, Ph.D. dissertation, Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA, [2] J. Raquet and R. Martin, Non-GNSS radio frequency navigation, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, March 2008, pp [3] Z. Kassas, Collaborative opportunistic navigation, IEEE Aerospace and Electronic Systems Magazine, vol. 28, no. 6, pp , [4] Z. Kassas, Analysis and synthesis of collaborative opportunistic navigation systems, Ph.D. dissertation, The University of Texas at Austin, USA, [5] J. McEllroy, Navigation using signals of opportunity in the AM transmission band, Master s thesis, Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, USA, [6] S. Fang, J. Chen, H. Huang, and T. Lin, Is FM a RF-based positioning solution in a metropolitan-scale environment? A probabilistic approach with radio measurements analysis, IEEE Transactions on Broadcasting, vol. 55, no. 3, pp , September [7] P. Thevenon, S. Damien, O. Julien, C. Macabiau, M. Bousquet, L. Ries, and S. Corazza, Positioning using mobile TV based on the DVB-SH standard, NAVIGATION, Journal of the Institute of Navigation, vol. 58, no. 2, pp , [8] C. Yang, T. Nguyen, and E. Blasch, Mobile positioning via fusion of mixed signals of opportunity, IEEE Aerospace and Electronic Systems Magazine, vol. 29, no. 4, pp , April [9] R. Faragher and R. Harle, Towards an efficient, intelligent, opportunistic smartphone indoor positioning system, NAVIGATION, Journal of the Institute of Navigation, vol. 62, no. 1, pp , [10] J. Khalife, Z. Kassas, and S. Saab, Indoor localization based on floor plans and power maps: Non-line of sight to virtual line of sight, in Proceedings of ION GNSS Conference, September 2015, pp [11] M. Ulmschneider and C. Gentner, Multipath assisted positioning for pedestrians using LTE signals, in Proceedings of IEEE/ION Position, Location, and Navigation Symposium, April 2016, pp [12] W. Xu, M. Huang, C. Zhu, and A. Dammann, Maximum likelihood TOA and OTDOA estimation with first arriving path detection for 3GPP LTE system, Transactions on Emerging Telecommunications Technologies, vol. 27, no. 3, pp , [13] J. Khalife, K. Shamaei, and Z. Kassas, Navigation with cellular CDMA signals part I: Signal modeling and software-defined receiver design, IEEE Transactions on Signal Processing, vol. 66, no. 8, pp , April [14] J. Khalife and Z. Kassas, Navigation with cellular CDMA signals part II: Performance analysis and experimental results, IEEE Transactions on Signal Processing, vol. 66, no. 8, pp , April [15] Z. Kassas, J. Morales, K. Shamaei, and J. Khalife, LTE steers UAV, GPS World Magazine, vol. 28, no. 4, pp , April [16] J. Khalife and Z. Kassas, Precise UAV navigation with cellular carrier phase measurements, in Proceedings of IEEE/ION Position, Location, and Navigation Symposium, April 2018, pp [17] Z. Kassas, J. Khalife, K. Shamaei, and J. Morales, I hear, therefore I know where I am: Compensating for GNSS limitations with cellular signals, IEEE Signal Processing Magazine, pp , September [18] J. del Peral-Rosado, J. Lopez-Salcedo, G. Seco-Granados, F. Zanier, and M. Crisci, Achievable localization accuracy of the positioning reference signal of 3GPP LTE, in Proceedings of International Conference on Localization and GNSS, June 2012, pp [19] K. Shamaei, J. Khalife, and Z. Kassas, Ranging precision analysis of LTE signals, in Proceedings of European Signal Processing Conference, August 2017, pp [20] K. Shamaei, J. Khalife, and Z. Kassas, Pseudorange and multipath analysis of positioning with LTE secondary synchronization signals, in Proceedings of Wireless Communications and Networking Conference, 2018, pp [21] C. Gentner, E. Munoz, M. Khider, E. Staudinger, S. Sand, and A. Dammann, Particle filter based positioning with 3GPP-LTE in indoor environments, in Proceedings of IEEE/ION Position, Location and Navigation Symposium, April 2012, pp [22] K. Shamaei, J. Khalife, and Z. Kassas, Comparative results for positioning with secondary synchronization signal versus cell specific reference signal in LTE systems, in Proceedings of ION International Technical Meeting Conference, January 2017, pp [23] M. Driusso, C. Marshall, M. Sabathy, F. Knutti, H. Mathis, and F. Babich, Vehicular position tracking using LTE signals, IEEE Transactions on Vehicular Technology, vol. 66, no. 4, pp , April [24] K. Shamaei, J. Khalife, and Z. Kassas, Exploiting LTE signals for navigation: Theory to implementation, IEEE Transactions on Wireless Communications, vol. 17, no. 4, pp , April [25] J. del Peral-Rosado, J. Lopez-Salcedo, G. Seco-Granados, F. Zanier, and M. Crisci, Evaluation of the LTE positioning capabilities under typical multipath channels, in Proceedings of Advanced Satellite Multimedia Systems Conference and Signal Processing for Space Communications Workshop, September 2012, pp [26] K. Shamaei and Z. Kassas, LTE receiver design and multipath analysis for navigation in urban environments, NAVIGATION, Journal of the Institute of Navigation, 2018, accepted. [27] R. van Nee, The multipath estimating delay lock loop, in Proceedings of Spread Spectrum Techniques and Applications Symposium, November 1992, pp [28] M. Psiaki, T. Ertan, B. O Hanlon, and S. Powell, GNSS multipath mitigation using antenna motion, NAVIGATION, Journal of the Institute of Navigation, vol. 62, no. 1, pp. 1 22, Spring 2015.

10 [29] P. Axelrad, C. Comp, and P. Macdoran, SNR-based multipath error correction for GPS differential phase, IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 2, pp , April [30] Y. D. Jong and M. Herben, High-resolution angle-of-arrival measurement of the mobile radio channel, IEEE Transactions on Antennas and Propagation, vol. 47, no. 11, pp , November [31] T. Pany, N. Falk, B. Riedl, C. Stober, J. Winkel, and H. Ranner, GNSS synthetic aperture processing with artificial antenna motion, in Proceedings of ION GNSS Conference, September 2013, pp [32] M. Yaqoob, F. Tufvesson, A. Mannesson, and B. Bernhardsson, Direction of arrival estimation with arbitrary virtual antenna arrays using low cost inertial measurement units, in Proceedings of International Conference on Communications Workshops, June 2013, pp [33] A. Broumandan, J. Nielsen, and G. Lachapelle, Narrowband signal detection in correlated Rayleigh fading with a moving antenna, in Proceedings of Antenna Technology and Applied Electromagnetics and the Canadian Radio Science Meeting, February 2009, pp [34] K. Pesyna, T. Humphreys, R. Heath, T. Novlan, and J. Zhang, Exploiting antenna motion for faster initialization of centimeteraccurate GNSS positioning with low-cost antennas, IEEE Transactions on Aerospace and Electronic Systems, vol. 53, no. 4, pp , August [35] A. Mourikis and S. Roumeliotis, A multi-state constraint Kalman filter for vision-aided inertial navigation, in Proceedings IEEE International Conference on Robotics and Automation, April 2007, pp [36] S. Kumar, P. Gupta, G. Singh, and D. Chauhan, Performance analysis of Rayleigh and Rician fading channel models using MATLAB simulation, International Journal of Intelligent Systems and Applications, vol. 5, no. 9, pp , August [37] J. Barnes, A. Chi, R. Andrew, L. Cutler, D. Healey, D. Leeson, T. McGunigal, J. Mullen, W. Smith, R. Sydnor, R. Vessot, and G. Winkler, Characterization of frequency stability, IEEE Transactions on Instrumentation and Measurement, vol. 20, no. 2, pp , May [38] Y. Bar-Shalom, X. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation. New York, NY: John Wiley & Sons, [39] J. Farrell and M. Barth, The Global Positioning System and Inertial Navigation. New York: McGraw-Hill, [40] P. Groves, The PNT boom: future trends in integrated navigation, Inside GNSS, pp , March/April [41] J. Morales, P. Roysdon, and Z. Kassas, Signals of opportunity aided inertial navigation, in Proceedings of ION GNSS Conference, September 2016, pp [42] 3GPP, Evolved universal terrestrial radio access (E-UTRA); user equipment (UE) radio transmission and reception, 3rd Generation Partnership Project (3GPP), TS , June [Online]. Available: [43] B. Yang, K. Letaief, R. Cheng, and Z. Cao, Timing recovery for OFDM transmission, IEEE Journal on Selected Areas in Communications, vol. 18, no. 11, pp , November [44] K. Shamaei, J. Khalife, S. Bhattacharya, and Z. Kassas, Computationally efficient receiver design for mitigating multipath for positioning with LTE signals, in Proceedings of ION GNSS Conference, September 2017, pp [45] P. Misra and P. Enge, Global Positioning System: Signals, Measurements, and Performance, 2nd ed. Ganga-Jamuna Press, [46] Z. Kassas and T. Humphreys, Observability analysis of collaborative opportunistic navigation with pseudorange measurements, IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp , February 2014.