A framework that could detect the presence of repetitive features in unknown signals is developed and experimentally demonstrated with non-terrestrial (NTN) sources, namely GPS and low Earth orbit (LEO) satellites. In modern communication systems, the existence of repetitive feature (i.e., the beacon) in the transmitted signals is inherited either implicitly; due to the transmitter’s source and channel coding, modulation, and multiplexing schemes; or explicitly, by broadcasting repetitive sequences for synchronization purposes. Upon detecting an unknown source, this framework, termed cognitive beacon detection and estimation, is capable of estimating the beacon, regardless of the adopted modulation scheme by the source. First, a model that is adequate to describe received signals from highly dynamic satellite channels is presented. The model parametrizes signals received from multiple unknown sources in terms of their time-varying carrier power, delay, Doppler, and user data. Second, a sequential beacon detection and estimation approach is developed, which is agnostic to the modulation scheme employed by the transmitter. The proposed cognitive framework allows for opportunistic exploitation of salient information embedded in the estimated beacon, such as beam, sector, and transmitter identification sequences. Third, the proposed framework is shown to converge almost surely to the correct beacon one at a time, until all sources are detected. Fourth, experimental results are presented demonstrating the efficacy of the developed cognitive framework in blindly detecting and estimating beacons of NTN sources, namely GPS and 5 LEO constellations: NOAA, Orbcomm, Iridium, Starlink, and OneWeb. These sources transmit in a diverse swath of modulation schemes: AM/FM,M-PSK, SC-FDMA, and OFDM. Finally, experimental results are presented demonstrating the applicability of the developed framework in navigating a ground vehicle with multi-constellation LEO satellite signals. The pseudorange rate measurements produced by the framework to 2 Orbcomm, 1 Iridium, 4 Starlink, and 1 OneWeb were fused with altimeter measurements via an extended Kalman filter to estimate the vehicle’s nearly 1 km trajectory, achieving a three-dimensional position root mean squared error of 4.15 m.

This letter presents the first stationary user equipment (UE)-based positioning framework that blindly estimates both always-on and on-demand 5G downlink signals with multiple 5G gNBs, transmitting on the same channel. The framework opportunistically exploits the full bandwidth of downlink 5G signals. In the acquisition stage, the maximum likelihood function of the beamformed signal is computed to differentiate the gNBs in both carrier frequency offset and angular domains, followed by blind beacon estimation of each gNB. Carrier-aided code delay tracking loops are developed to track the carrier phase and code delay to extract navigation observables. The Cram´ er–Rao lower bounds (CRLBs) for carrier frequency offset, azimuth, and elevation angles are derived. Experimental results are presented demonstrating that the estimated beacon exhibits a narrower autocorrelation function compared to that of always-on signals, due to the exploitation of on-demand signals, leading to higher time resolution estimation. Monte Carlo (MC)–based positioning results for a stationary UE using pseudorange and azimuth angle measurements from two co-channel 5G gNBs are presented. With initial positions randomly located within a 99% confidence circle of 2.1 km radius, the mean final position estimate across MC realizations was within 8.6 m of the true UE position.

Radio simultaneous localization and mapping (SLAM) with terrestrial signals of opportunity (SOPs) in a real-world GPS-denied environment is demonstrated. First, radio SLAM with known and unknown SOP maps is formulated. In the former case, the receiver's spatial states are estimated alongside the receiver-SOPs temporal states; while in the latter, the receiver's and SOPs' spatial states are estimated alongside the receiver-SOPs temporal states. Next, experimental results are presented of a vehicle navigating at Edwards Air Force Base, California, USA, while GPS signals were under intentional jamming. The vehicle exploited 7 terrestrial cellular SOP transmitters in the environment to navigate for 5 km in 180 seconds, with one of the SOPs more than 25 km away. Monte Carlo radio SLAM results with various levels of SOP position uncertainty are presented, from fully-known to poorly-known with 95% uncertainty ellipses of radii 7.8 m, 24.5 m, 77.4 m, and 245 m, achieving a 2D position position root-mean squared error (RMSE) of 2.6 m, 16.8, 23.6, 39.4 m, and 44.3 m, respectively.

A method to improve the carrier-to-noise ratio$(C/N_{0})$of received cellular fifth-generation (5G) signals via beamforming is developed. The developed method utilizes the direction-of-arrival (DOA) estimated from an antenna array, which is processed and fed to the receiver's tracking loops estimating the time-of-arrival (TOA). Experimental results are presented demonstrating a$C/N_{0}$improvement of about 7 dB in a stationary scenario and 6 dB in a moving scenario, compared to a non-beamformed approach. Using fused TOA and DOA measurements from two 5G gNBs via an extended Kalman filter (EKF), it is shown that starting from an initial position estimate 470 m away, the receiver can localize itself to within 10.6 m. In the moving scenario, fusing non-beamformed TOA measurements to two 5G gNBs via an EKF yielded a two-dimensional position root mean-squared error (RMSE) of 8.1 m, while using beamformed TOA measurements reduced the RMSE to 5.4 m.

The problem of position estimation using range measurements from transmitters with inaccurately known positions is considered. The true position of each transmitter is assumed to lie within a disk of a known radius, centered at the inaccurate position. A robust estimation framework is proposed, formulating a min-max optimization problem and presenting a tractable solution approach. A sensitivity map is constructed to quantify the positioning error in different regions due to inaccuracies in the transmitters’ positions. Numerical simulations are presented demonstrating the construction and application of the sensitivity map for estimating the position of a mobile receiver. It is shown that the sensitivity map yields invaluable insights to the expected positioning error in various regions within the environment. Experimental results are presented of a vehicle navigating in a real-world GPS-jammed environment using pseudorange measurements from 7 cellular transmitters whose positions are inaccurately known. The vehicle’s positioning error is justified utilizing the offline-generated sensitivity map.

An opportunistic navigation receiver that exploits always-on and on-demand 5G downlink signals is presented. The semi-cognitive receiver operates in two stages: (i) acquisition, which utilizes the "always-on" signals to detect gNBs, and (ii) Kalman filter (KF)-based tracking, which continuously estimates the on-demand reference signals (RSs) to refine the receiver’s local replica. Experimental results show that the estimated replica effectively utilizes nearly the entire channel bandwidth, spans almost all orthogonal frequency division multiplexing (OFDM) symbols for longer integration time, and achieves higher processing gain, enhancing the carrier-to-noise ratio for reliable acquisition and tracking. An experiment with real 5G signals on a ground vehicle demonstrated a significant 62% reduction in position root-mean squared error (RMSE) compared to a conventional opportunistic navigation 5G receiver which only utilized always-on signals.

A localization upper-bound with range measurements to transmitters whose positions are uncertain is derived. The transmitters’ positions uncertainty is known to be bounded by a disk with a known radius ε. First, assuming that only one of the transmitters’ positions is uncertain (while the rest are perfectly known), an analytic solution is derived for the inaccurate transmitter position within the uncertainty disk, which maximizes the ranging error. This leads to establishing a localization error upper-bound. To demonstrate the applicability of the upper-bound to the case of multiple uncertain transmitter positions, numerical simulations are presented demonstrating that the derived upper-bound still holds. Experimental results are presented of a vehicle navigating in GPS-denied environment, making pseudorange measurements to 7 cellular transmitters, whose positions are uncertain. The experimental results demonstrate the applicability of the upper-bound in predicting the worst-case vehicle localization performance.

The fundamental tracking performance in radio navigation is characterized, leading to optimal receiver design considerations. First, a generalized beacon model is proposed and its sufficient, salient parameters are defined. Second, closed-form approximations of the delay, Doppler, Doppler stretch, and Doppler rate ambiguity functions (AFs) and a generalized coherent integration efficiency model are proposed. Third, design considerations and optimal coherent processing interval (CPI) length selection are presented, based on the complete navigation framework, entailing the: (i) beacon’s parameters, (ii) channel dynamics between the transmitter and receiver, (iii) employed clocks by the transmitter and receiver, (iv) search space adopted in the acquisition stage, and (v) dynamical model’s order employed in the tracking stage. Fourth, the acquisition and tracking stages of the navigation receiver architecture are discussed. Fifth, three sets of experimental results are presented validating the proposed closed-form approximation of the Doppler stretch and Doppler rate AFs and demonstrating the performance of a receiver tuned by the proposed design considerations in acquiring, tracking, and localization, namely: (i) aircraft tracking of terrestrial 4G signals, (ii) stationary receiver tracking of Starlink low Earth orbit (LEO) signals, and (iii) stationary receiver localization with Starlink and OneWeb LEO signals. For the Starlink and OneWeb receiver localization experiment, the receiver was capable of tracking the Doppler and carrier phase of 5 Starlink and 3 OneWeb LEO satellites. Starting from an initial estimate 50 km away from its true position, the receiver converged to a final two-dimensional (2D) position error of 30.3 m.

A receiver architecture for cognitive sensing and navigation with orthogonal frequency division multiplexing (OFDM)-based systems is proposed. The proposed receiver enables exploiting all the transmitted periodic beacons of 5G new radio (NR) and Starlink low Earth orbit (LEO) signals to draw navigation observables. Reference signals (RSs) of modern OFDM-based systems, such as 5G NR, contain both always-on and on-demand components. These components can be unknown or known but subject to change. To leverage all transmitted signals for navigation purposes, the RS signals should be detected and tracked cognitively. Similar to conventional navigation receivers, the proposed architecture involves acquisition and tracking stages. However, both stages are supplemented by the unorthodox capability of estimating and updating the RS signals. The acquisition stage instructs the tracking stage by reporting performance metrics, which are used to adjust the tracking loop gains to update the RS accordingly. A chirp model is considered to capture the high dynamics of Doppler frequency in intensive Doppler scenarios, where the navigating vehicle is maneuvering or the transmitting source is not static. The effect of Doppler rate estimation error on frame length estimation is analyzed. Experimental results are presented demonstrating the performance of the proposed receiver by: (i) enabling an unmanned aerial vehicle (UAV) to detect and exploit terrestrial 5G NR cellular signals in a blind fashion for navigation purposes, achieving a two-dimensional (2D) root-mean squared error (RMSE) of 4.2 m over a total trajectory of 416 m; (ii) enabling a ground vehicle that traversed a trajectory of 1.79 km to cognitively sense an unknown gNB (blindly detect, track, and exploit transmitted always-on and on-demand signals), localizing it with a 2D error of 5.83 m; and (iii) tracking Starlink LEO OFDM signals, producing Doppler measurements, which were fused to localize a stationary receiver with a 2D error of 6.5 m, starting from an initial estimate 179 km away from the receiver’s true position.

A computationally efficient approach for acquisition and Doppler tracking for positioning, navigation, and timing (PNT) with unknown low Earth orbit (LEO) megaconstellation satellite signals is developed. The acquisition is based on a sequential matched subspace detector (MSD), whose complexity is reduced via: (i) reducing the detector's projection matrix calculation from$\mathcal {O}(N^{2}_{s})$to$\mathcal {O}(N_{s})$, where$ N_{s}$is typically on the order of$10^{5}$; and (ii) exploiting the computational efficiency of the fast Fourier transform (FFT) to mechanize the likelihood function calculation. The Doppler Cramér-Rao lower bound as a function of the signal-to-noise ratio is derived and the performance of the proposed approach is analyzed. A Kalman filter is developed to track the LEO Doppler frequency. The significance of utilizing sequential MSD to acquire multiple LEO satellites is experimentally demonstrated with OneWeb LEO signals. Experimental results are presented with Starlink LEO satellite signals, showing successful acquisition and Hz-level Doppler tracking. Positioning results with six Starlink LEO satellites are presented showing a two-dimensional error of 9.52 m, starting from an initial estimate 179 km away.

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.

A blind Doppler spectral approach is proposed for exploiting unknown Starlink low Earth orbit (LEO) satellite signals for positioning. First, an analytical derivation of the received signal frequency spectrum is presented, which accounts for the highly dynamic channel between the LEO satellite and a ground-based receiver. Second, a frequency domain-based blind Doppler discriminator is proposed. Third, a Kalman filter (KF)-based Doppler tracking algorithm is developed. Finally, experimental results are presented of a stationary receiver tracking the Doppler, in a blind fashion, of six Starlink LEO satellites over a period of about 800 seconds with Hz-level accuracy. The Doppler measurements were fused through a nonlinear least-squares estimator to localize the receiver to an unprecedented level of accuracy. Starting with an initial estimate 200 km away, the proposed approach achieved a final horizontal two-dimensional (2D) position error of 4.3 m.

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.

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.

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.

The ability of reinforcement learning (RL)-based convolutional neural network (CNN) to mitigate multipath signals for opportunistic navigation with downlink 5G signals is assessed. The CNN uses inputs from the autocorrelation function (ACF) to learn the errors in the code phase estimates. A ray tracing algorithm is used to produce high fidelity training data that could model the dynamics between the line of sight (LOS) component and the non-line of sight (NLOS) components. Experimental results on a ground vehicle navigating with 5G signals for 902 m in a multipath-rich environment are presented, demonstrating that the proposed RL-CNN achieved a position root-mean squared error (RMSE) of 14.7 m compared to 20.6 m with a conventional delay-locked loop (DLL).

An interacting multiple-model (IMM) estimator is developed to adaptively estimate the process noise covariance of low Earth orbit (LEO) satellite clocks for improved positioning. Experimental results are presented showing a stationary ground receiver localizing itself with carrier phase measurements from a single Orbcomm LEO satellite. The developed IMM is shown to reduce the localization error and improve filter consistency over two fixed mismatched extended Kalman filters (EKFs). Starting with an initial receiver position error of 1.45 km, the IMM yielded a final error of 111.26 m, while the errors of a conservative and optimistic EKFs converged to 254.71 m and 429.35 m, respectively.

Detection of unknown beacons of signals of opportunity (SOPs), whether terrestrial or from low Earth orbit (LEO) satellites is considered. The abundance of SOPs promises an attractive alternative to global navigation satellite system (GNSS) signals. However, some specifications of ambient SOPs may not be available to the public. In this paper, a computationally efficient cognitive opportunistic navigation framework is proposed to (i) detect constrained unknown beacons and (ii) estimate their Doppler frequencies. Experimental results are presented demonstrating the efficacy of the proposed framework in successful detection and Doppler tracking for both terrestrial cdma2000 signals and Orbcomm LEO satellite signals.

A receiver autonomous integrity monitoring (RAIM) framework for ground vehicle navigation using ambient cellular signals of opportunity (SOPs) and an inertial measurement unit (IMU) is developed. The proposed framework accounts for two types of errors that compromise the integrity of the navigation solution: (i) multipath and (ii) unmodeled biases in the cellular pseudorange measurements due to line-of-sight (LOS) signal blockage and high signal attenuation. This paper, first, characterizes the multipath in a cellular-based navigation framework. Next, a fault detection and exclusion technique for a cellular-based navigation framework is developed. Simulation and experimental results with real long-term evolution (LTE) signals are presented evaluating the efficacy of the proposed RAIM-based fault detection and exclusion technique on a ground vehicle navigating in a deep urban environment in the absence of global navigation satellite system (GNSS) signals. The experimental results on a ground vehicle traversing 825 m in an urban environment show that the proposed RAIM-based measurement exclusion technique reduces the position root mean-squared error (RMSE) by 66%.

Information fusion strategies for vehicles navigating while aiding their inertial navigation systems (INSs) with terrestrial signals of opportunity (SOPs) are developed and studied. The following problem is considered. Multiple navigating vehicles with access to global navigation satellite system (GNSS) signals are aiding their on-board INSs with GNSS pseudoranges. While navigating, vehicle-mounted receivers draw pseudorange measurements from terrestrial SOPs (e.g., AM/FM radio, digital television, cellular) with unknown emitter positions and unknown and unsynchronized clocks. The vehicles share INS data and SOP pseudoranges to collaboratively estimate the SOPs’ states through an extended Kalman filter using tight coupling. After some time, GNSS signals become unavailable, at which point the navigating vehicles use shared INS and SOP information to continue navigating in a collaborative inertial radio simultaneous localization and mapping (CIRSLAM) framework. This paper develops such CIRSLAM framework and synthesizes what SOP and INS information should be shared between collaborators. Two information fusion strategies are compared: 1) sharing time-of-arrival (TOA) measurements from SOPs; 2) sharing time-difference-of-arrival (TDOA) measurements taken with reference to an SOP. Next, a strategy to efficiently share INS information along with SOP information is discussed. Monte Carlo simulation results are presented that support the analytical findings that vehicles navigating in a CIRSLAM framework, while sharing and fusing SOP TOA measurements, produce a smaller or equal estimation error covariance compared to fusing SOP TDOA measurements. Experimental results are presented demonstrating two unmanned aerial vehicles (UAVs) navigating in a CIRSLAM framework with SOP TOA measurements from terrestrial cellular towers. The final UAVs’ localization error after 30 seconds of GPS unavailability were reduced compared to using an INS alone from around 55 m to around 6 m.

An extensive experimental study to characterize frequency range 1 (FR1) (i.e., sub-6 GHz) 5th generation (5G) signals from existing infrastructure for navigation is presented. The study uses a state-of-the-art 5G navigation software-defined radio (SDR) to track 5G signals in different environments and under different conditions to analyze the behavior of the received carrier-to-noise-ratio (C/N0), which directly affects the precision of the navigation performance. Three different experimental scenarios were conducted for this purpose with real 5G signals and 4th generation (4G) long-term evolution (LTE) signals for comparison purposes: (i) a stationary indoor scenario to study the effect of wall and floor partitions, (ii) a stationary outdoor scenario to study the effect of sampling rate, antenna grade, and clock quality, and (iii) a mobile outdoor experiment to study the C/N0as a function of the range. All three scenarios confirmed the potential of downlink 4G and 5G signals for navigation.

An algorithm for blind Doppler frequency estimation from orthogonal frequency division multiplexing (OFDM) signals transmitted by low Earth orbit (LEO) satellites is developed. A method for resolving the ambiguity in the Doppler estimate is also discussed. Two sets of experimental results are presented. The first demonstrates blind Doppler estimation from terrestrial fifth-generation (5G) signals on a mobile ground vehicle, achieving 14.5 Hz Doppler root mean-squared error (RMSE). The second demonstrates an unmanned aerial vehicle navigating using the proposed approach with emulated 5G signals from two Orbcomm LEO satellites for a period of 2 minutes, achieving 15.16 m position RMSE.

A method for reducing the protection levels (PLs) of aerial vehicles by fusing global navigation satellite systems (GNSS) signals with terrestrial signals of opportunity (SOPs) is developed. PL is a navigation integrity parameter that guarantees the probability of position error exceeding a certain value to be bounded by a target integrity risk. For unmanned aerial vehicles (UAVs), it is desirable to achieve as tight PLs as possible. This paper characterizes terrestrial cellular SOPs' measurement errors from extensive UAV flight campaigns, collected over the past few years in different environments and from different providers, transmitting at different frequencies and bandwidths. Next, the reduction in PLs due to fusing terrestrial SOPs with a traditional GNSS-based navigation system is analyzed. It is demonstrated that incorporating terrestrial SOP measurements is more effective in reducing the PLs over adding GNSS measurements. Experimental results are presented for a UAV traversing a trajectory of 823 m, during which the VPL of the GPS-based and GNSS-based navigation systems were reduced by 56.9% and 58.8%, respectively, upon incorporating SOPs; while the HPL of the GPS-based and GNSS-based navigation systems were reduced by 82.4% and 74.6%, respectively, upon incorporating SOPs.

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.

A receiver autonomous integrity monitoring (RAIM)-based framework for autonomous ground vehicle (AGV) navigation is developed. This framework aims to incorporate terrestrial signals of opportunity (SOPs) alongside GPS signals to provide tight horizontal protection level (HPL) bounds to enhance the safety of autonomous driving. The performance of the combined GPS-SOP system is analyzed. Simulation results show that the combined GPS-SOP system reduces the HPL significantly from a GPS-only system, particularly in poor user-to-satellite geometry conditions. It is also shown that adding SOPs is more effective in increasing RAIM availability as opposed to adding GNSS satellites. Experimental results show that the GPS-SOP system reduces the HPL by 44.77% from the HPL obtained by GPS-only.

A ground vehicle navigation approach in a global navigation satellite system (GNSS)-challenged environments is developed, which uses signals of opportunity (SOPs) in a closed-loop map-matching fashion. The proposed navigation approach employs a particle filter that estimates the ground vehicle's state by fusing pseudoranges drawn from ambient SOP transmitters with road data stored in commercial maps. The problem considered assumes the ground vehicle to have a priori knowledge about its initial states as well as the position of SOPs. The proposed closed-loop approach estimates the vehicle's states for subsequent time as it navigates without the GNSS signals. In this approach, a particle filter is employed to continuously estimate the vehicle's position and velocity along with the clock error states of the vehicle-mounted receiver and SOP transmitters. The simulation and experimental results with cellular long-term evolution (LTE) SOPs are presented, evaluating the efficacy and accuracy of the proposed framework in different driving environments. The experimental results demonstrate a position root-mean-squared error (RMSE) of: 1.6 m over a 825-m trajectory in an urban environment with five cellular LTE SOPs, 3.9 m over a 1.5-km trajectory in a suburban environment with two cellular LTE SOPs, and 3.6 m over a 345-m trajectory in a challenging urban environment with two cellular LTE SOPs. It is demonstrated that incorporating the proposed map-matching algorithm reduced the position RMSE by 74.88%, 58.15%, and 46.18% in these three environments, respectively, from the RMSE obtained by an LTE-only navigation solution.

A framework for positioning with low Earth orbit (LEO) satellite signals is proposed. The framework employs an extend Kalman filter (EKF) to estimate a receiver's position using Doppler frequency measurements from LEO satellites. The satellites' positions and velocities are known through two-line element (TLE) files. A receiver architecture to acquire and track LEO satellite signals and extract Doppler measurements to LEO satellites is discussed. Simulation results show that 11 m positioning accuracy can be achieved with 25 LEO satellites. Experimental results are presented demonstrating the proposed stationary receiver estimating its position using Doppler measurements from 2 Orbcomm LEO satellites with an accuracy of 360 m over a 1 minute period.

The Performance of cellular long-term evolution (LTE) signals for indoor localization is evaluated. Two different designs of LTE software-defined receivers (SDRs), namely a code phase-based receiver and a carrier phase-based receiver, are presented and assessed experimentally indoors with LTE signals. A base/navigator framework is presented to deal with the unknown clock biases of the LTE eNodeBs. In this framework, the base receiver is placed outdoors, has knowledge of its own position, and makes pseudorange measurements to eNodeBs in the environment whose positions are known. The base transmits these pseudoranges to the indoor navigating receiver, which is also making pseudorange measurements to the same eNodeBs. The navigating receiver differences the base's and navigator's pseudoranges; hence, the unknown eNodeBs' biases are eliminated. The navigator receiver is equipped with an inertial measurement unit (IMU), and the LTE pseudoranges and IMU measurements are tightly coupled using an extended Kalman filter (EKF). Two sets of experimental results are presented. First, it is demonstrated that the standalone carrier phase-based receiver yielded a more precise navigation solution than the code phase-based receiver, specifically a two-dimensional (2-D) position root mean-squared error (RMSE) of 5.09 m versus 11.76 m for an indoor trajectory of 109 m traversed in 50 seconds. Second, it is demonstrated that coupling the IMU with the carrier phase-based LTE receiver reduced the 2-D position RMSE to 2.92 m. Moreover, it is demonstrated that the proposed LTE-IMU system yielded a maximum error of 5.60 compared to 22.53 m for the IMU-only.

An indoor pedestrian localization system which is based on cellular long-term evolution (LTE) carrier phase measurements and synthetic aperture navigation (SAN) is developed. The proposed system relies on a moving antenna array to determine the direction-of-arrival (DOA) of received LTE signals, while suppressing multipath signals. Two schemes are studied to couple LTE carrier phase measurements with SAN: (1) feedforward LTE-SAN and (2) feedback LTE-SAN. The performance of both coupling schemes is validated experimentally in a challenging indoor environment, in which the proposed system traversed a distance of 126.8 m in 100 seconds, while receiving LTE signals from 6 eNodeBs. The position root mean-squared error (RMSE) exhibited by the proposed LTE-SAN approach was 5.20 m and 4.32 m with feedforward and feedback coupling, respectively, compared with 7.19 m using a standalone LTE approach.

A framework for simultaneously tracking Orbcomm low Earth orbit (LEO) satellites and using Doppler measurements drawn from their signals to aid a vehicle's inertial navigation system (INS) is developed. The developed framework enables a navigating vehicle to exploit ambient Orbcomm LEO satellite signal Doppler measurements to aid its INS in a tightly-coupled fashion in the event global navigation satellite system (GNSS) signals become unusable. An overview of the extended Kalman filter-based simultaneous tracking and navigation (STAN) framework is provided. Experimental results are presented showing an unmanned aerial vehicle (UAV) aiding its INS with Doppler measurements drawn from two Orbcomm LEO satellites, reducing the final position error from 31.7 m to 8.9 m after 30 seconds of GNSS unavailability.

Unmanned aerial vehicle (UAV) trajectory planning in urban environments is considered. Equipped with a three- dimensional (3-D) environment map, the UAV navigates by fusing global navigation satellite systems (GNSS) signals with ambient cellular signals of opportunity. A trajectory planning approach is developed to allow the UAV to reach a target location, while constraining its position uncertainty and multipath- induced biases in cellular pseudo-ranges to be below a desired threshold. Experimental results are presented demonstrating that following the proposed trajectory yields a reduction of 30.69% and 58.86% in the position root-mean squared error and the maximum position error, respectively, compared to following the shortest trajectory between the start and target locations.

A navigation framework based on a multi-state constraint Kalman filter (MSCKF) is proposed to reduce the effect of time-correlated pseudorange measurement noise of cellular long-term evolution (LTE) signals. The proposed MSCKF framework captures the position of the antenna over a window of measurements to impose constraints on the position estimate. Simulation results are presented showing a reduction of 57% and 51% in the two- dimensional (2D) and three-dimensional (3D) position root mean squared-error (RMSE), respectively, using the proposed framework compared to an extended Kalman filter (EKF). Experimental results on a ground vehicle navigating in an urban environment are presented showing a reduction of 29% and 64.7% in the 2D and 3D position RMSE, respectively, and a reduction of 19.6% and 86.7% in the 2D and 3D maximum error, respectively, using the proposed framework compared to an EKF.

A distributed, low communication rate architecture is proposed for collaborating vehicles to aid their inertial navigation systems (INSs) with cellular signals. The proposed approach compresses the amount of communicated data between vehicles by invoking mild approximations that reduce the communication rate by 91.7% from an optimal centralized approach, with a negligible impact on performance. Simulation and experimental results are presented demonstrating multiple unmanned aerial vehicles (UAVs) navigating via the proposed framework, aiding their INSs with cellular pseudoranges in the absence of GPS.

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%.

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.

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.

The observability analysis of a collaborative opportunistic navigation (COpNav) environment whose states may be partially known is considered. A COpNav environment can be thought of as a radio frequency (RF) signal landscape within which one or more RF receivers locate themselves in space and time by extracting and, possibly, sharing information from ambient signals of opportunity (SOPs). These receivers, whether vehicle mounted or integrated into handheld devices, exploit signal diversity to improve navigation and timing robustness compared with stand-alone Global Positioning System (GPS) receivers in deep urban, indoor, or, otherwise, GPS-hostile environments. Available SOPs may have a fully known, partially known, or unknown characterization. In this paper, the receivers are assumed to draw only pseudorange-type measurements from the SOPs. Separate observations are fused to produce an estimate of each receiver's position, velocity, and time (PVT). Since not all SOP states in the COpNav environment may be known a priori, the receivers must estimate the unknown SOP states of interest simultaneously with their own PVT. This paper establishes the minimal conditions under which a COpNav environment consisting of multiple receivers and multiple SOPs is completely observable. Moreover, in scenarios where the COpNav environment is unobservable, the unobservable directions in the state space are specified. Simulation and experimental results are presented to confirm the theoretical observability conditions.

This paper presents an algorithm for in-surveillance and out-of-surveillance mobile ground target tracking. In this respect, a nonlinear Bayesian estimation filter is presented. Then, an adaptive algorithm is derived to reduce remarkably the computational burden of this filter, while not degrading the accuracy of the state estimates. Additionally, an algorithm employing the concepts of hospitability and synthetic inclination maps is introduced and is coupled with the nonlinear Bayesian filter to track the mobile ground target once it goes out-of-surveillance. Loosely speaking, the hospitability map can be viewed as a terrain-based map defining a likelihood or a “weight” for each point on the earth's surface proportional to the ability of the target to move and maneuver at that location. On the other hand, the synthetic inclination map describes how the target favors certain regions within the search area, hence being “synthetically” inclined to move toward them.

The inherent problem of the Global Positioning System (GPS), which is signal obstruction, remains the major obstacle that inhibits it from functioning as a "reliable stand-alone" positioning system. Therefore, it is becoming a common practice to couple the GPS system with an external positioning system whenever the GPS receiver is expected to operate in regions of dense canopy, such as urban areas. Commercial automobile navigation systems currently employ a GPS receiver coupled with a dead reckoning (DR) system and a map-matching algorithm. Most DR systems, which compensate for GPS inaccuracies and frequent GPS signal obstructions, employ an odometer and a directional sensor. In this paper, a power matching approach is proposed for GPS coverage extension in urban area. The algorithm, based on a statistical measure, correlates the received power from different GPS satellite vehicles (SVs), which leads to a specific signature, i.e., to a topographical database with periodic time-varying estimates of the received powers of SVs. An experimental approach is presented to examine the feasibility of applying the proposed positioning system.