Opportunistic navigation with cellular signals is studied and demonstrated on a high-altitude aircraft. An extensive campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation Laboratory (ASPIN) in collaboration with the U.S. Air Force (USAF) to sample ambient cellular signals of opportunity (SOPs) at different altitudes in different regions in Southern California, USA. The carrier-to-noise ratio is characterized as a function of altitude and horizontal distance. It was found that signals from cellular transmitters can be reliably acquired and tracked for navigation purposes by receivers mounted on high dynamics aircraft flying at altitudes as high as 23,000 ft above ground level and horizontal distances as far as 100 km. The multipath channel is also characterized as a function of altitude, showing clean channels with a dominant line-of-sight component for altitudes up to 23,000 ft. To further assess the potential of cellular SOPs for aircraft navigation, a 51-km trajectory traversed over a period of 9 minutes was estimated exclusively using cellular SOPs (i.e., with no GPS or other sensors, except for barometric altimeter measurements), resulting in a three-dimensional position (3-D) 10.5-m position root-mean-squared error.

Assessment of Cellular Signals of Opportunity for High-Altitude Aircraft Navigation

Developing a universal pedestrian navigation framework that operates through extreme environmental conditions is essential. Such a navigation framework can enable Location-Based Services (LBS) in many applications, and one application in high demand of accurate and reliable positioning solutions is firefighter localization, primarily for navigating in indoor environments where signals of Global Navigation Satellite Systems (GNSS) might degrade or fail, visibility is poor, and infrastructure dedicated to navigation is often not accessible. Jao et al. (2022a) reported a Pedestrian Indoor Navigation system integrating Deterministic, Opportunistic, and Cooperative localization approaches (PINDOC). The deterministic localization is a Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation System (INS) enhanced with self-contained aiding approaches, including altimeter measurements and foot-to-foot ranging measurements. The opportunistic approach uses pseudorange measurements extracted from cellular Long-Term Evolution (LTE) towers and implements a Deep Neural Network (DNN)-based Synthetic Aperture Navigation (SAN) to spatially mitigate multipath. This approach operates in a base/rover framework, where a GNSS receiver and a "base" LTE receiver, both installed stationary in an outdoor environment, are used to estimate clock bias drifts of LTE towers, and the estimated clock biases are transmitted to "rover" LTE receivers equipped on agents navigating in indoor environments. The cooperative localization approach uses UWBs for inter-agent range measurements and differentiates Line-Of-Sight (LOS) and NLOS components using a power-metric-based detector. In this paper, we experimentally investigate the navigation performance of the PINDOC system. Two experiments were conducted. The first experiment involved three agents, with one agent traversing in an indoor environment a trajectory of 600 meters in 14 minutes, during which the other two agents remained stationary. The traversed trajectory included terrains of flat surfaces, stairs, ramps, and elevators. The PINDOC system achieved a position Root-Mean-Squared Error (RMSE), maximum error, and loop-closure error of 0.93 m, 2.23 m, and 1.28 m over the 600-meter trajectory, respectively. In the second experiment, all three agents traveled in the indoor environment for 12.5 minutes, and the navigation solutions estimated by the PINDOC system showed loop-closure errors of 0.35 m, 0.82, and 1.15 m for the three agents. In all cases, access to signals of opportunity and cooperative exchange of information between agents were available less than 20% of time for duration of the experiments.

Sub-Meter Accurate Pedestrian Indoor Navigation System with Dual ZUPT-Aided INS, Machine Learning-Aided LTE, and UWB Signals

The positioning system based on the signals of opportunity (SOPs) of low-Earth-orbit (LEO) satellites can meet the positioning requirements of the receiver in the global navigation satellite system (GNSS) denial environment. However, the received signal does not contain or cannot extract the orbit information and signal emission time of the radiation source, which has become the main error source in this positioning system. Taking the Iridium satellites as an example, a Doppler differential (DD)-LEO (DD-LEO) positioning framework is proposed in this article, and the baseline impact analysis of the differential Doppler positioning system (DDPS) and a performance improvement method are carried out. First, the workflow between a base and a rover is introduced, and the error elimination of the DDPS is analyzed. Second, the equivalent Doppler error models formed by the orbit error at the base and the rover are established, respectively, and the relationship between the baseline and the mean or variance of equivalent Doppler residual in the DDPS is derived. Then, aiming at the problem of poor positioning accuracy due to the inconsistency of space–time reference in the long-baseline DDPS, a signal emission time estimation (SETE) algorithm based on maximum likelihood estimation (MLE) is proposed to further improve the positioning accuracy. Finally, a simulation experiment of the influence of baseline length and direction on the DDPS is implemented, and several groups of field differential experiments with different baseline lengths are completed. The experimental results of long-baseline positioning show that the DDPS can reduce the 3-D positioning error by 48.0% compared with the single station without differential positioning. At the same time, the positioning accuracy of the rover station is further enhanced by 86.3% when the base station adopts the SETE algorithm, which verifies the effectiveness of the proposed algorithm.

Analysis of Baseline Impact on Differential Doppler Positioning and Performance Improvement Method for LEO Opportunistic Navigation

A Kalman filter-based receiver autonomous integrity monitoring algorithm (RAIM) is proposed to exploit sequential measurements from global navigation satellite systems (GNSS) and cellular 5G signals of opportunity (SOPs), to ensure safe vehicular navigation in urban environments. To deal with frequent threats caused by multipath and non-line-of-sight conditions, an innovation-based outlier rejection method is introduced. Next, a fault detection technique based on solution separation test is developed, and the quantification of protection levels is derived. Experimental results of a ground vehicle traveling in an urban environment, while making pseudorange measurements to GPS satellites and cellular 5G towers, are presented to demonstrate the efficacy of the proposed method. Incorporating 5G signals from only 2 towers is shown to reduce the horizontal protection level (HPL) by 0.22 m compared to using only GPS. Moreover, the proposed method is shown to reduce the HPL and vertical protection level (VPL) by 84.42% and 69.63%, respectively, over the snapshot advanced RAIM (ARAIM). 

Kalman Filter-Based Integrity Monitoring for GNSS and 5G Signals of Opportunity Integrated Navigation

The role of vehicular applications in the design of future 6G infrastructures

I am not afraid of the GPS jammer, as long as there are ambient signals of opportunity (SOPs) to exploit in the environment. In environments where GPS signals are challenged (e.g., indoors and deep urban canyons) or denied (e.g., under jamming and spoofing attacks), SOPs could serve as an alternative positioning, navigation, and timing (PNT) source to GPS, and more generally, to global navigation satellite systems (GNSS). This article presents a radio simultaneous localization and mapping (radio SLAM) approach that enables the exploitation of SOPs for resilient and accurate PNT. Radio SLAM estimates the states of the navigator-mounted receiver simultaneously with the SOPs’ states. Radio SLAM could produce an SOP-derived navigation solution in a standalone fashion or by fusing SOPs with sensors [e.g., inertial measurement unit (IMU), lidar, etc.], digital maps, and/or other signals (e.g., GNSS). This article presents the first published experimental results evaluating the efficacy of radio SLAM in a real GPS-denied environment. These experiments took place at Edwards Air Force Base, California, USA, during which GPS was intentionally jammed with jamming-to-signal ratio as high as 90 dB. This article evaluates the timing of two cellular long-term evolution (LTE) SOPs located in the jammed environment, showing timing stability over 95 min of GPS jamming. Moreover, the article presents navigation results showcasing a ground vehicle traversing a trajectory of about 5 km in 180 s in the GPS-jammed environment. The vehicle's GPS-IMU system drifted from the vehicle's ground truth trajectory, resulting in a position root mean-squared error (RMSE) of 238 m. In contrast, the radio SLAM approach with a single cellular LTE SOP whose position was poorly known (an initial uncertainty on the order of several kilometers) achieved a position RMSE of 32 m.

I Am Not Afraid of the GPS Jammer: Resilient Navigation Via Signals of Opportunity in GPS-Denied Environments

In this paper, we present a Pedestrian Indoor Navigation system integrating Deterministic, Opportunistic, and Cooperative functionalities (PINDOC) for multi-agent navigation. The deterministic module produces a navigation solution for each agent by utilizing a Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation System (INS) augmented with an altimeter and foot-to-foot range measurements. The opportunistic module augments the deterministic solutions with pseudorange measurements extracted from cellular Long-Term Evolution (LTE) signals. The navigation accuracy of each individual agent is further enhanced by cooperative localization using Ultra-WideBand (UWB)-based inter-agent range measurements. We developed a dedicated multi-sensor pedestrian navigation hardware testbed and conducted an experiment to evaluate the navigation performance of different combinations of components comprising PINDOC: ZUPT-aided INS, altimeter, foot-to-foot ranging, LTE signals, and inter-agent ranging. The experiment involved three agents, with one agent traversing a trajectory of 600 meters in 14 minutes, during which, the other two agents remained stationary. The traversed trajectory included terrains of flat surfaces, stairs, ramps, and elevators. The PINDOC system showed high accuracy, achieving a position Root-Mean-Squared Error (RMSE), maximum error, and final error of 0.93 m, 2.23 m, and 1.28 m over the 600-meter trajectory.

PINDOC: Pedestrian Indoor Navigation System Integrating Deterministic, Opportunistic, and Cooperative Functionalities

A user equipment (UE)-based navigation framework that opportunistically exploits 5G signals is developed. The proposed framework exploits the “always on” 5G downlink signals in a time-domain-based receiver. To this end, a so-called ultimate synchronization signal (USS) is proposed to utilize the time-domain orthogonality of the orthogonal frequency division multiplexing (OFDM)-based 5G signals. This approach simplifies the receiver's complexity and enhances the performance of the 5G opportunistic navigation framework. Experimental results are presented to evaluate the efficacy of the proposed framework on a ground vehicle navigating in a suburban environment, while utilizing sub-6 GHz 5G signals from two gNBs. It is shown that while a state-of-the-art frequency-domain-based 5G opportunistic navigation receiver can only reliably track the gNBs' signals over a trajectory of 1.02 km traversed in 100 seconds, producing a position root mean-squared error (RMSE) of 14.93 m; the proposed time-domain-based receiver was able to track over a trajectory of 2.17 km traversed in 230 seconds, achieving a position RMSE of 9.71 m.

Opportunistic Navigation Using Sub-6 GHz 5G Downlink Signals: A Case Study on A Ground Vehicle

Predicting the safety of urban roads for navigation via global navigation satellite systems (GNSS) signals is considered. To ensure the safe driving of automated vehicles, a vehicle must plan its trajectory to avoid navigating on unsafe roads (e.g., icy conditions, construction zones, narrow streets, and so on). Such information can be derived from roads’ physical properties, the vehicle’s capabilities, and weather conditions. From a GNSS-based navigation perspective, the reliability of GNSS signals in different locales, which is heavily dependent on the road layout within the surrounding environment, is crucial to ensure safe automated driving. An urban road environment surrounded by tall objects can significantly degrade the accuracy and availability of GNSS signals. This article proposes an approach to predict the reliability of GNSS-based navigation to ensure safe urban navigation. Satellite navigation reliability at a given location and time on a road is determined based on the probabilistic position error bound of the vehicle-mounted GNSS receiver. A metric for GNSS reliability for ground vehicles is suggested, and a method to predict the conservative probabilistic error bound of the GNSS navigation solution is proposed. A satellite navigation reliability map is generated for various navigation applications. As a case study, the reliability map is used in a proposed optimization problem formulation for automated ground vehicle safety-constrained path planning.

Urban Road Safety Prediction: A Satellite Navigation Perspective

A framework that could achieve submeter-level-accurate horizontal navigation with carrier phase differential measurements from cellular signals is developed. This framework, termed CD-cellular, is composed of a base and a rover in a cellular environment, both making carrier phase measurements to the same cellular base transceiver stations (BTSs). The base shares its carrier phase measurements with the mobile rover, which in turn employs an extended Kalman filter to obtain a coarse estimate of its states, followed by a batch weighted nonlinear least squares (B-WNLS) estimator to solve for the integer ambiguities, and finally a point-solution WNLS to estimate its own states. The framework is designed to guarantee that after some time, the rover's position error remains below a pre-defined threshold with a desired probability. This is achieved by leveraging models of the BTS positions from stochastic geometry. Experimental results on an unmanned aerial vehicle (UAV) in an open semi-urban environment with multipath-free, line-of-sight (LOS) conditions are presented, showing that the developed framework achieves a 70.48 cm position root mean-squared error (RMSE) over a trajectory of 2.24 km, measured with respect to the UAV's navigation solution from its onboard GPS-inertial navigation system (INS).

Zaher M. Kassas

Papers

I Am Not Afraid of the GPS Jammer: Resilient Navigation Via Signals of Opportunity in GPS-Denied Environments

PINDOC: Pedestrian Indoor Navigation System Integrating Deterministic, Opportunistic, and Cooperative Functionalities

Opportunistic Navigation Using Sub-6 GHz 5G Downlink Signals: A Case Study on A Ground Vehicle

Urban Road Safety Prediction: A Satellite Navigation Perspective

Differential Framework for Submeter-Accurate Vehicular Navigation With Cellular Signals