Joe Khalife

There has been significant interest over the past few years in integrated sensing and communication (ISAC) to enable applications, such as the massive Internet of Things, highly automated transportation systems, and military surveillance. Many ISAC studies in the literature considered designing, from scratch, next-generation systems that are endowed with ISAC capabilities. This article argues that “the truth is out there” and that one can sense and exploit unknown signals, whether non-ISAC “legacy” or ISAC devised. The article presents a framework termed cognitive sensing and opportunistic navigation ( COSON ). COSON can be thought of as an instantiation of ISAC, but, instead of having the “luxury” of designing signals with ISAC capabilities, COSON senses arbitrary, unknown communication signals and exploits them for positioning, navigation, and timing purposes. COSON is composed of four stages: 1) Blind signal acquisition, which comprises spectrum sensing and signal activity detection, blind beacon estimation, initial Doppler and Doppler rate estimation, and blind source enumeration; 2) blind signal tracking and beacon refinement; 3) interference and multipath classification; and 4) sensing and navigation. Extensive experimental results are presented, demonstrating the broad applicability of COSON to terrestrial and nonterrestrial sources, transmitting with various modulation and multiple access schemes: cellular 4G and 5G signals, GPS, and low-Earth orbit satellite (Starlink, Orbcomm, and Iridium) signals. COSON is demonstrated to localize stationary antennas and navigate unmanned aerial vehicles and a ground vehicle, to meter-level accuracy, without global navigation satellite system signals.

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

A radio simultaneous localization and mapping (radio SLAM) framework enabling aircraft navigation with terrestrial signals of opportunity (SOPs) is presented and experimentally validated. The framework does not assume availability of global navigation satellite system (GNSS) signals. Instead, it assumes the aircraft to have an initial estimate of its own states, after which it navigates by exploiting pseudorange measurements extracted from terrestrial SOPs, while estimating the states of the aircraft simultaneously with the SOPs’ states. Two radio SLAM frameworks are presented: (i) tightly-coupled SOP-aided inertial navigation system (INS) and (ii) utilizing a Wiener process acceleration (WPA) dynamical model for the aircraft’s dynamics instead of the INS. Results from four flight runs on a US Air Force C-12 aircraft, equipped with an altimeter and an industrial-grade inertial measurement unit (IMU), are presented. The flight runs took place over semi-urban (SU), urban (U), and rural (R) regions in California, USA; while exercising different aircraft maneuvers: holding (H), descending (D), and grid (G). Different a priori conditions of the SOPs’ positions were studied: from all unknown, to some known, to all known. In all cases, the SOPs’ clock error states (bias and drift) were unknown and estimated alongside the aircraft’s states. The results consistently demonstrated the promise of real-world aircraft navigation via radio SLAM, yielding bounded errors along trajectories of tens of kilometers. The three-dimensional (3–D) position root-mean squared errors (RMSEs) are summarized next, where $N$ denotes the number of SOPs exploited along the trajectory: (1) SU, H, INS-SOP, $N=6$ , 56.7 km in 8.5 minutes, maximum altitude of 5,577 ft: 43.27 m with all unknown and 10.14 m with all known; (2) U, H, INS-SOP, $N=6$ , 72.7 km in 12.9 minutes, maximum altitude of 5,906 ft: 89.82 m with all unknown and 16.97 m with all known; (3) SU, D, WPA-SOP, $N=18$ , 111.9 km in 20.0 minutes, maximum altitude of 6,234 ft: 36.42 m with all unknown and 18.62 m with all known; and (4) R, G, WPA-SOP, $N=32$ , 78.4 km in 13.8 minutes, maximum altitude of 7,546 ft: 67.01 m with all unknown and 25.65 m with all known.

Integrity monitoring of a vehicular navigation system that utilizes multi-constellation global navigation satellite systems (GNSS) signals fused with terrestrial signals of opportunity (SOPs) is considered. An opportunistic advanced receiver autonomous integrity monitoring (OARAIM) framework is developed to detect faults and calculate protection levels (PLs). The influence of fusing SOPs on the integrity performance is analyzed. It is shown that fusing a single SOP with GNSS signals essentially increases both the horizontal PL (HPL) and vertical PL (VPL), while fusing two or more SOPs could reduce the PLs and improves fault detection. Performance sensitivity analysis for the probability of SOP fault and user range error is conducted to characterize the fault-free HPL under different regimes. Experimental results on an unmanned aerial vehicle (UAV) navigating with GPS signals fused with cellular SOPs are presented to validate the effectiveness of the OARAIM framework and demonstrate the analysis of the integrity performance in the horizontal direction.

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.

A cognitive approach is proposed to detect unknown beacons of terrestrial signals of opportunity (SOPs). Two scenarios are considered in the paper: (i) detection of unknown beacons with integer constraints (IC) and (ii) detection of unknown beacons with no integer constraint (NIC). An example of beacons with IC is the pseudo-noise (PN) sequences in cellular code division multiple access (CDMA) signals. On the other hand, the reference signals (RSs) in orthogonal frequency-division multiplexing (OFDM)-based systems can be considered as beacons signals with NIC. Matched subspace detectors are proposed for both scenarios, and it is shown experimentally that the proposed matched subspace detectors are capable of detecting cellular third-generation (3G) cdma2000 signals and fifth-generation (5G) OFDM signals. A low complexity method is derived to simplify the matched subspace detector with IC for M -ary phase shift keying ( M PSK) modulation. The effect of symbol errors in the estimated beacon signal on the carrier to noise ratio (CNR) is characterized analytically. Closed-form expressions for the asymptotic probability of detection and false alarm are derived. Experimental results are presented showing an application of the proposed cognitive approach by enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial cellular signals for navigation purposes. In one experiment, the UAV achieved submeter-level accurate navigation over a trajectory of 1.72 km, by exploiting signals from four 3G cdma2000 transmitters. In another experiment, the UAV achieves a position root mean-squared error (RMSE) of 4.63 m over a trajectory of 416 m, by exploiting signals from two 5G transmitters.

A reduced-order multiple-model (MM) estimator for noise identification in dynamic stochastic systems is developed. The unknown noise statistics are assumed to be static. While a standard static MM estimator does not grow exponentially over time, its computational complexity grows exponentially with the number of modes. The proposed algorithm reduces the computational complexity of MM estimation from exponential to polynomial by constructing a significantly smaller set of mode models which are updated every time step. It is assumed that the constructed mode models do not change significantly between time steps, which in turn holds if the smoothness of the mode probabilities is guaranteed. It is shown that in the case where there is “enough” statistical distinction between the noise modes, the proposed reduced-order MM estimator converges to the standard MM estimator. The proposed reduced-order MM estimator is evaluated using Monte Carlo simulations, showing that it performs nearly similar to the standard MM estimator with a fraction of its complexity. The numerical example shows less than a 2% increase in the root mean-squared errors (RMSEs) of the reduced-order MM estimator from the standard one, while the reduction in the number of filters in the reduced-order MM estimator is 300%. To further validate the proposed filter, experimental results are presented of an unmanned aerial vehicle (UAV) navigating with terrestrial signals of opportunity. Opportunistic navigation serves a relevant application for MM-based estimation, as system parameters, namely the statistics of the clock error dynamics of opportunistic sources, are unknown and must be adaptively estimated. The experimental results show a UAV navigating for more than 5 minutes over a trajectory of more than 3 km, achieving a final position error of 6.21 m obtained using the standard MM estimator versus a final position error of 6.25 m obtained using the proposed reduced-order MM estimator. A standard extended Kalman filter (EKF) was implemented for comparative analysis, showing a final error of 40.03 m. In the experiments, the reduced-order MM estimator was implemented with 16 filters, while the standard MM was implemented with 256 filters.

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.

A navigation framework with carrier phase differential measurements from megaconstellation low Earth orbit (LEO) satellite signals is developed. The measurement errors due to ephemeris errors and ionospheric and tropospheric delays are derived and the statistics of the dilution of precision is characterized. Moreover, the joint probability density function of the megaconstellation LEO satellites' azimuth and elevation angles is derived to (i) enable performance characterization of navigation frameworks with LEO satellites in computationally efficient way and (ii) facilitate parameter design, namely, the differential baseline, to meet desired performance requirements. The Starlink constellation is used as a specific LEO megaconstellation example to demonstrate the developed carrier phase differential LEO (CD-LEO) navigation framework. Simulation results are presented demonstrating the efficacy of the proposed CD-LEO framework for an unmanned aerial vehicle (UAV) navigating for 15.1 km in 300 seconds, while using signals from 44 Starlink satellites, achieving a three-dimensional (3-D) position root mean squared error (RMSE) of 2.2 m and a 2-D RMSE of 32.4 cm. Experimental results are presented showing UAV navigating for 2.28 km in 2 minutes over Aliso Viejo, California, USA, using exclusively signals from only two Orbcomm LEO satellites, achieving an unprecedented position RMSE of 14.8 m.