Joe Khalife

A software-defined receiver architecture for cellular CDMA-based navigation

A detailed software-defined receiver (SDR) architecture 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 navigation and timing purposes. The differences between a typical GPS receiver and the proposed cellular CDMA receiver are highlighted. Moreover, a framework that is based on a mapping/navigating receiver scheme for navigation in a cellular CDMA environment is studied. The position and timing errors arising due to estimating the base transceiver station clock biases in different cell sectors are also analyzed. Experimental results utilizing the proposed SDR are presented demonstrating a mean distance difference of 5.51 m from a GPS navigation solution.

I Hear, Therefore I Know Where I Am: Compensating for GNSS Limitations with Cellular Signals

Global navigation satellite systems (GNSSs) have been the prevalent positioning, navigation, and timing technology over the past few decades. However, GNSS signals suffer from four main limitations.

Ranging precision analysis of LTE signals

The ranging precision of the secondary synchronization signal in cellular long-term evolution (LTE) systems is evaluated. First, the pseudorange error for a delay-locked loop with a coherent baseband discriminator is analyzed, and a closed-form expression for the standard deviation of the pseudorange error is derived. Second, the effect of multipath on the ranging error is evaluated analytically. Experimental results closely matching the analytical expression of the pseudorange error standard deviation are presented. Key remarks to take into consideration when designing a receiver for positioning using LTE signals are provided throughout the paper.

Modeling and analysis of sector clock bias mismatch for navigation with cellular signals

The clock bias in different sectors of a cellular base transceiver station (BTS) cell is studied. A dynamical model relating the clock biases in different BTS sectors is identified and validated experimentally. A theoretical estimation error lower bound due to the discrepancy between sector clock biases is derived and demonstrated against Monte Carlo simulation runs. Experimental results of an unmanned aerial vehicle (UAV) navigating exclusively with cellular code division multiple access signals are presented. These results demonstrate a 10.51m reduction in the UAVs position estimation error due to incorporating the developed sector mismatch model into the estimation framework.

Exploiting LTE Signals for Navigation: Theory to Implementation

Exploiting cellular long-term evolution (LTE) downlink signals for navigation purposes is considered. First, the transmitted LTE signal model is presented and relevant positioning and timing information that can be extracted from these signals are identified. Second, a software-defined receiver (SDR) that is capable of acquiring, tracking, and producing pseudoranges from LTE signals is designed. Third, a threshold-based approach for detecting the first peak of the channel impulse response is proposed in which the threshold adapts to the environmental noise level. This method is demonstrated to be robust against noise and interference in the environment. Fourth, an approach for estimating pseudoranges of multiple base stations by tracking only one base station is proposed. Fifth, a navigation framework based on an extended Kalman filter is proposed to produce the navigation solution using the pseudorange measurements obtained by the proposed SDR. Finally, the proposed SDR is evaluated experimentally on an unmanned aerial vehicle (UAV) and a ground vehicle. The root mean squared-error (RMSE) between the GPS navigation solution and LTE signals from three base stations produced by the proposed SDR for the UAV is shown to be 8.15 m with a standard deviation of 2.83 m. The RMSE between the GPS navigation solution and LTE signals from six base stations in a severe multipath environment for the ground vehicle is shown to be 5.80 m with a standard deviation of 3.02 m.

Pose estimation with lidar odometry and cellular pseudoranges

A pose estimation framework by fusing light detection and ranging (lidar) odometry measurements and cellular pseudoranges using an extended Kalman filter is proposed. Iterative closest point (ICP) is used to solve for the relative pose between lidar scans. A maximum likelihood estimator is developed for lidar scan registration. The proposed framework works with few ICP iterations; hence, can be used for real-time applications. The framework is tested experimentally, and it is demonstrated that the two-dimensional position root mean square error obtained with ICP only can be reduced by 93.58% by fusing lidar odometry and cellular pseudoranges.