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Session B1: Receiver Signal Processing

A Joint TOA and DOA Approach for Positioning with LTE Signals
Kimia Shamaei and Zak (Zaher) M. Kassas, University of California, Riverside
Location: Cypress

Exploiting long-term evolution (LTE) signals, the fourth generation of cellular systems, for navigation has received increased attention recently due to LTE’s inherently desirable characteristics: abundance, large transmission bandwidth, high received power, frequency diversity, and geometric favorability of transmitter location [1]. The network-based positioning capability of LTE was enabled in Release 9 by introducing the positioning reference signal (PRS). In a network-based approach, the relative timing differences between the received signals from the serving cell and the neighboring cells are transmitted to the location server, where a time-difference-of-arrival (TDOA) approach is used to estimate the position of the user equipment (UE). A network-based positioning approach suffers from a number of drawbacks: (1) the user’s privacy is compromised since the user’s location is revealed to the network [2], (2) localization services are limited only to paying subscribers and from a particular cellular provider, (3) ambient LTE signals transmitted by other cellular providers are not exploited, and (4) additional bandwidth is required to accommodate the PRS, which caused the majority of cellular providers to choose not to transmit the PRS in favor of dedicating more bandwidth for traffic channels. Due to the aforementioned drawbacks of the network-based approach, alternative UE-based approaches have been studied recently, where several software-defined receivers (SDRs) are proposed to obtain the time-of-arrival (TOA) of LTE signals [3-6]. The results have demonstrated meter-level navigation accuracy in environments without severe multipath with real and laboratory-emulated LTE signals [7, 8].
One of the main challenges in UE-based navigation with LTE signals is the unknown clock biases of the base stations (also known as Evolved Node Bs or eNodeBs). Current approaches to overcome this challenge include: (1) estimating and removing the clock bias in a post-processing fashion by using the known position of the UE [7, 9], (2) using perfectly synchronized eNodeBs in laboratory-emulated LTE signals [4], and (3) estimating the difference of the clock biases of the UE and each eNodeB in an extended Kalman filter (EKF) framework [6]. The first approach does not provide an on-the-fly navigation solution in GPS-challenged environments. The second approach is not feasible with real LTE signals. In the third approach, the initial value of the receiver’s state, namely the receiver’s position and velocity as well as the receiver’s clock bias and drift, must be known (e.g., from GNSS) in order to make the problem observable. This initial knowledge might not be available in many practical scenarios, e.g., at receiver cold-start in the absence of GNSS signals. To address the aforementioned challenge, a joint TOA and direction-of-arrival (DOA) estimation is developed in this paper to estimate the receiver’s position and clock bias as well as the eNodeBs’ clock biases by exploiting both temporal and spatial diversity of TOA and DOA, respectively. Not only the proposed joint TOA and DOA approach will enable navigation in environments where GNSS signals are initially unavailable, but it will also offer the complementary benefits of TOA and DOA. This will yield more accurate navigation solution and robustness to multipath.
The problem of joint angle and delay estimation (JADE) was first addressed in [10, 11], where multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) were used to jointly estimate the delay and angle [12, 13]. MUSIC and ESPRIT are two statistical techniques, which are based on the eigen-structure of the covariance matrix. These algorithms were obtained based on the assumption of noncoherent received signals. Therefore, in the presence of multipath coherent signals, additional signal processing must be performed [14]. In contrast to the MUSIC and ESPRIT algorithms, the matrix pencil (MP) approach works directly with data and does not need additional signal processing in the presence of multipath coherent signals [15, 16]. The literature on positioning using joint angle and delay estimation are either (1) based on simulation results, where the clock bias is not considered [17] or (2) network-based approaches, where joint TDOA and DOA estimation are exploited to estimate the UE’s position [18]. However, in UE-based positioning approaches with real LTE signals, neither the UE’s clock nor the eNodeBs’ clocks are known to the UE. Therefore, they must be estimated along with the UE’s position.
This paper makes three contributions. First, it develops a navigation framework based on joint TOA and DOA estimation to estimate the receiver’s position and clock bias as well as the eNodeBs’ clock biases on-the-fly. Second, it presents the first experimental demonstration of positioning with LTE signals via the joint TOA and DOA estimation framework. Third, the effect of the number of antennas, the transmission bandwidth, and different approaches (e.g. MUSIC, ESPRIT, and MP) on the estimation accuracy is evaluated.
The structure of the paper is organized as follows. First, the LTE signal model is provided and the ranging signals are described. Second, different algorithms to jointly estimate the TOA and DOA (i.e., MUSIC, ESPRIT, MP algorithms) are discussed. Third, the navigation framework to estimate the receiver’s position and clock bias and the eNodeBs’ clock biases is presented. Fourth, the effect of the number of antennas and transmission bandwidth on the estimation accuracy is analyzed. Finally, experimental results are presented comparing the achievable localization performance with different estimation approaches versus the proposed approach.
References
[1] Z. Kassas, J. Khalife, K. Shamaei, and J. Morales, “I hear, therefore I know where I am: Compensating for GNSS deficiencies with cellular signals.” IEEE Signal Processing Magazine, pp. 111–124, September 2017.
[2] M. Hofer, J. McEachen, and M. Tummala, “Vulnerability analysis of LTE location services,” in Proceedings of Hawaii International Conference on System Sciences, January 2014, pp. 5162–5166.
[3] 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. 301–308.
[4] J. del Peral-Rosado, J. Lopez-Salcedo, G. Seco-Granados, F. Zanier, P. Crosta, R. Ioannides, and M. Crisci, “Software-defined radio LTE positioning receiver towards future hybrid localization systems,” in Proceedings of International Communication Satellite Systems Conference, October 2013, pp. 14–17.
[5] 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. 3376–3391, April 2017.
[6] 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, accepted.
[7] F. Knutti, M. Sabathy, M. Driusso, H. Mathis, and C. Marshall, “Positioning using LTE signals,” in Proceedings of Navigation Conference in Europe, April 2015, pp. 1–8.
[8] 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. 1256–1268.
[9] M. Driusso, F. Babich, F. Knutti, M. Sabathy, and C. Marshall, “Estimation and tracking of LTE signals time of arrival in a mobile multipath environment,” in Proceedings of International Symposium on Image and Signal Processing and Analysis, September 2015, pp. 276–281.
[10] M. Vanderveen, C. Papadias, and A. Paulraj, “Joint angle and delay estimation (JADE) for multipath signals arriving at an antenna array,” IEEE Communications Letters, vol. 1, no. 1, pp. 12–14, January 1997.
[11] M. Vanderveen, A. V. der Veen, and Paulraj, “Estimation of multipath parameters in wireless communications,” IEEE Transactions on Signal Processing, vol. 46, no. 3, pp. 682–690, March 1998.
[12] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276–280, March 1986.
[13] R. Roy and T. Kailath, “ESPRIT-estimation of signal parameters via rotational invariance techniques,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 7, pp. 984–995, July 1989.
[14] T. Shan, M. Wax, and T. Kailath, “On spatial smoothing for direction-of-arrival estimation of coherent signals,” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 33, no. 4, pp. 806–811, August 1985.
[15] Y. Hua and T. Sarkar, “Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, no. 5, pp. 814–824, May 1990.
[16] Y. Hua, “Estimating two-dimensional frequencies by matrix enhancement and matrix pencil,” IEEE Transactions on Signal Processing, vol. 40, no. 9, pp. 2267–2280, September 1992.
[17] M. Navarro and M. Najar, “TOA and DOA estimation for positioning and tracking in IR-UWB,” in Proceedings of IEEE International Conference on Ultra-Wideband, September 2007, pp. 574–579.
[18] A. Gaber and A. Omar, “A study of wireless indoor positioning based on joint TDOA and DOA estimation using 2-D matrix pencil algorithms and IEEE 802.11ac,” IEEE Transactions on Wireless Communications, vol. 14, no. 5, pp. 2440–2454, May 2015.



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