Ali A. Abdallah

Aircraft navigation without global navigation satellite system (GNSS) signals is considered. To this end, a high sensitivity receiver design is presented that could exploit terrestrial cellular signals of opportunity. The receiver operates on downlink orthogonal frequency division multiplexing (OFDM) signals transmitted by long-term evolution (LTE) eNodeBs. Two challenges encountered on high altitude aircraft are addressed: weak signal power and insufficiently accurate initial Doppler estimation. A so-called ultimate reference signal is proposed, which exploits all available LTE resources transmitted by eNodeB's multiple antenna ports. A time-domain-based receiver design is presented to extract carrier phase observables without the need to reconstruct the received OFDM frame. The proposed approach is shown to significantly improve the receiver's sensitivity, amplifying the received power by nearly 21 dB, while also improving the carrier phase estimation accuracy. To evaluate the efficacy of the proposed receiver, experimental results are presented of an aircraft flying over two regions in California, USA: (i) Region A: Edwards Air Force Base (rural) and (ii) Region B: Palmdale (semi-urban). For three aircraft maneuvers (climbing teardrop, descending teardrop, and grid), the results were consistent: more than 100 eNodeBs were trackable, some of which more than 100 km away, at altitudes as high as about 11,000 ft with carrier-to-noise ratio over 40 dB-Hz. Upon fusing the carrier phase observables with altimeter data via an extended Kalman filter, a sustained accurate navigation solution was achieved. Over trajectories of 43.6 km and 56.8 km in regions A and B, traversed in 455 s and 601 s, a three-dimensional position root mean-squared error (RMSE) of 6.8 m and 8.2 was achieved by exploiting an average of about 22 and 11 eNodeBs, respectively

This paper presents a receiver that could exploit downlink orthogonal frequency-division multiplexing (OFDM)-based cellular signals to navigate opportunistically to meter-level accuracy in a real-world GPS-denied environment. The proposed receiver exploits signals from multiple logical antenna ports simulta-neously, which dramatically improves the receiver's sensitivity. The efficacy of the proposed receiver is demonstrated experimentally in an environment under intentional GPS jamming, in which the ground vehicle-mounted receiver navigated for 5 km in 180 seconds. The receiver was able to acquire and track signals from 7 long-term evolution (LTE) eNodeBs, one of which was more than 25 km away, achieving a two-dimensional position root mean-squared error (RMSE) of 2.6 m.

This article presents the first demonstration of navigation with cellular signals of opportunity (SOPs) on a high-altitude aircraft. An extensive flight campaign was conducted by the Autonomous Systems Perception, Intelligence, and Navigation Laboratory in collaboration with the U.S. Air Force to sample ambient downlink cellular SOPs in different regions in Southern California, USA. Carrier phase measurements were produced from these signals, which were subsequently fused in an extended Kalman filter along with altimeter measurements to estimate the aircraft’s state (position, velocity, and time). Three flights are performed in three different regions: 1) rural, 2) semiurban, and 3) urban. A multitude of flight trajectories and altitudes above ground level (AGL) was exercised in the three flights: 1) a 51-km trajectory of grid maneuvers with banking and straight segments at about 5,000 ft AGL, 2) a 57-km trajectory of a teardrop descent from 7,000 ft AGL down to touchdown at the runway, and 3) a 55-km trajectory of a holding pattern at about 15,000 ft AGL. The estimated aircraft trajectory is computed for each flight and compared with the trajectory from the aircraft’s onboard navigation system, which utilized a GPS receiver coupled with an inertial navigation system and an altimeter. The cellular SOPs produced remarkable sustained navigation accuracy over the entire flight trajectories in all three flights, achieving a 3D position root mean-squared error of 10.53 m, 4.96 m, and 15.44 m, respectively.

This letter presents the first user equipment (UE)-based 5G navigation framework that exploits the “on-demand” 5G downlink signals. In this framework, the entire system bandwidth of incoming 5G signals is utilized in an opportunistic fashion. The proposed framework involves a cognitive approach to acquire the so-called ultimate reference signal (URS), which includes the “on-demand” as well as “always-on” reference signals (RSs). Experimental results are presented showing that the acquired URS: (i) spans the entire 5G downlink bandwidth, (ii) increases the carrier-to-noise ratio by 10 dB compared to state-of-the-art 5G user equipment (UE)-based opportunistic navigation receiver, and (iii) reduces significantly the carrier and code phase errors. A ranging error standard deviation of 2.75 m was achieved with proposed framework with a stationary receiver placed 290 m away from a 5G gNB in a clear line-of-sight environment, which is lower than the 5.05 m achieved when using the “always-on” 5G downlink signals.

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.

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.

A signal-sensor-based indoor pedestrian navigation system is developed. The proposed system: 1) utilizes a foot-mounted inertial navigation system (INS), in which the accumulated errors are mitigated via a zero velocity update (ZUPT) approach; 2) exploits opportunistically cellular long-term evolution (LTE) signals in a deep neural network (DNN)-based synthetic aperture navigation (SAN) framework, in which the pedestrian’s motion is utilized to suppress multipath-induced errors. The proposed DNN-SAN-LTE-ZUPT-INS (DUALS) indoor pedestrian navigation system utilizes the complementary the desirable characteristics of both subsystems, coupled via two architectures: (a) loosely-coupled and (b) tightly-coupled. This paper designs and assesses both architectures experimentally in an indoor environment. The experimental study demonstrates a pedestrian traversing a trajectory of 600 m in 14 minutes, including a stationary period, straight segments, up/down the stairs, and riding in an elevator, while receiving signals from 4 LTE base stations (also known as evolved node Bs (eNodeBs)). The proposed tightly-coupled DUALS system exhibited a three-dimensional (3-D) position root mean-squared error (RMSE) of 1.34 m, outperforming the loosely-coupled DUALS, ZUPT-aided INS, and LTE-DNN-SAN, which achieved a position RMSE of 1.38, 1.49, and 1.97 m, respectively.

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.