Kimia Shamaei

A receiver structure is proposed to jointly estimate the time-of-arrival (TOA) and azimuth and elevation angles of direction-of-arrival (DOA) from received cellular long-term evolution (LTE) signals. In the proposed receiver, a matrix pencil (MP) algorithm is used in the acquisition stage to obtain a coarse estimate of the TOA and DOA. Then, a tracking loop is proposed to refine the estimates and jointly track the TOA and DOA changes. The performance of the acquisition and tracking stages are evaluated in the presence of noise and multipath. Simulation results are provided to validate the analytical results. The Cramér-Rao lower bounds (CRLBs) of the TOA and DOA estimates are derived to compare the performance of the proposed acquisition and tracking approaches with the best-case performance. It is shown that the proposed approach has lower complexity compared to the MP algorithm. Finally, experimental results are provided with real LTE signals, showing a reduction of 93%, 57%, and 31% in the standard deviation of TOA, azimuth, and elevation angles' estimation errors, respectively, using the proposed receiver compared to the MP algorithm.

A comprehensive approach for opportunistic navigation with fifth-generation (5G) cellular signals that exploits the downlink channel is developed. The structure of possible 5G reference signals that can be exploited is presented. Then, a software-defined receiver (SDR) to extract navigation observables from cellular 5G signals is proposed. The statistics of the code phase error in a multipath-free environment and in the presence of multipath are derived, which are subsequently used to analyze the statistics of the position estimation error with different simulated channel models. Finally, experimental results are conducted to evaluate the ranging performance of the proposed SDR with real 5G signals. After removing the effect of the clock bias and drift from the estimated pseudorange, the ranging error standard deviation is shown to be 1.19 m.

Cellular fifth-generation (5G) signals are assessed for opportunistic navigation. A carrier-aided code-based software-defined receiver (SDR) is presented, which produces navigation observables from received downlink 5G signals. These observables are analyzed to assess the performance of 5G signals for opportunistic navigation. Experimental results are presented of a ground vehicle navigating with the 5G SDR while receiving signals from two 5G base stations (known as gNBs). It is shown that over a trajectory of 1.02 km traversed in 100 seconds, the position root mean-squared error (RMSE) was 14.9 m.

A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs), a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System (GNSS) receiver is developed. This framework aims to enable localization in an urban environment where GNSS signals could be unusable or unreliable. The proposed framework employs an extended Kalman filter (EKF) to fuse pseudorange observables extracted from cellular SOPs, IMU measurements, and GNSS-derived position estimates (when available). The EKF is coupled with a map-matching approach. The framework assumes the positions of the cellular towers to be known, and it estimates the vehicle's states (position, velocity, orientation, and IMU biases) along with the difference between the vehicle-mounted receiver clock error states (bias and drift) and each cellular SOP clock error state. The proposed framework is evaluated experimentally on a ground vehicle navigating in a deep urban area with a limited sky view. Results show a position root-mean-square error (RMSE) of 2.8 m across a 1,380-m trajectory when GNSS signals are available and an RMSE of 3.12 m across the same trajectory when GNSS signals are unavailable for 330 m. Moreover, compared to localization with a loosely coupled GNSS?IMU integrated system, a 22% reduction in the localization error is obtained whenever GNSS signals are available, and an 81% reduction in the localization error is obtained whenever GNSS signals are unavailable.

Mitigating multipath of cellular long‐term evolution (LTE) signals for robust positioning in urban environments is considered. A computationally efficient receiver, which uses a phase‐locked loop (PLL)–aided delay‐locked loop (DLL) to track the received LTE signals, is presented. The PLL‐aided DLL uses orthogonal frequency division multiplexing (OFDM)–based discriminator functions to estimate and track the time‐of‐arrival. The code phase and carrier phase performances in an additive white Gaussian noise (AWGN) channel are evaluated numerically. The effects of multipath on the code phase and carrier phase are analyzed, demonstrating robust multipath mitigation for high transmission LTE bandwidths. The average of the DLL discriminator functions over multiple LTE symbols is presented to reduce the pseudorange error. The proposed receiver is evaluated on a ground vehicle in an urban environment. Experimental results show a root mean square error (RMSE) of 3.17 m, a standard deviation of 1.06 m, and a maximum error of 6.58 m between the proposed LTE receiver and the GPS navigation solution over a 1.44 km trajectory. The accuracy of the obtained pseudoranges with the proposed receiver is compared against two algorithms: estimation of signal parameters by rotational invariance techniques (ESPRIT) and EKAT (ESPRIT and Kalman filter).

The ranging precision of the long-term evolution (LTE) secondary synchronization signal (SSS) with noncoherent baseband discriminators is analyzed. The open-loop and closed-loop statistics of the code phase error with the dot-product and early-power-minus-late-power discriminator are derived. The effect of multipath on the code phase error is evaluated numerically. Experimental results demonstrating the efficacy of the derived statistics are presented, in which the total position root-mean squared error (RMSE) with SSS over a 560 m ground vehicle trajectory was reduced by 51%.

An approach to jointly estimate the time-of-arrival (TOA) and azimuth and elevation angles of the direction-of-arrival (DOA) from received cellular long-term evolution (LTE) signals is developed. The approach uses a uniform planar antenna array and an extended Kalman filter (EKF) to estimate the receiver's position and difference between its clock bias and drift with those of each of the eNodeBs' without any a priori knowledge about the receiver's state. Simulation results evaluating the performance of the proposed method are presented for different transmission bandwidths, carrier-to-noise ratios, and number of antennas. Experimental results show an accuracy of 0.55 m in estimating the position of the receiver with real LTE signals.

A software-defined receiver (SDR) for navigation using cellular code-division multiple access (CDMA) signals is presented. The cellular forward-link signal structure is described and models for the transmitted and received signals are developed. Particular attention is paid to relevant information that could be extracted and subsequently exploited for positioning and timing purposes. The pseudorange from the proposed receiver is modeled and the pseudorange error is studied in an additive white Gaussian channel. Experimental results utilizing the proposed SDR are presented demonstrating a close match between the variation in pseudoranges and the variation in true ranges between the receiver and two cellular CDMA base transceiver stations (BTSs). Moreover, the dynamics of the discrepancy between the observed clock biases of different sectors of the same BTS cell is modeled and validated experimentally. The consistency of the obtained model is analyzed through experimental tests in different locations, at different times, and for different cellular providers.