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Abstract The joint utilization of the Fifth Generation Communications Technology (5G) and the Global Navigation Satellite System (GNSS) serves as a promising solution to address the challenges associated with insufficient visible satellites and lower observation quality in urban environments. 5G allows for the angle and distance measurements, augmenting the performance of Real-Time Kinematic (RTK) positioning. To quantify the improvement of 5G observations on RTK positioning, this paper proposes a float solution gain factor and the Ambiguity Dilution of Precision (ADOP) gain factor. Based on these gain factors, the theoretical analysis and simulation are performed. This study designs an extended Kalman filter for 5G-assisted BeiDou Navigation Satellite System (BDS) RTK positioning, employing both the Full Ambiguity Resolution (FAR) and Partial Ambiguity Resolution (PAR) modes. Our experiment verified the effectiveness of 5G-assisted BDS RTK positioning in mitigating outlier occurrences and improving the ambiguity fixing rate as well as the positioning accuracy. In the FAR and PAR modes, the Three-Dimensional (3D) spatial accuracy increased by 48% and 18.8%, respectively, and the results are consistent with theoretical analysis based on gain factors. The fixing rate of RTK increased from 11.11% to 13.93%, while it increased from 32.58% to 44.43% for the PAR mode. The assistance of 5G observations reduced the median error for the FAR mode from over 1.3m to 0.9 m, and the third quartile from 2.1m to 1.05 m. For the PAR mode, the median error decreased from 0.5m to 0.12 m, and the third and fourth quartiles decreased from 0.65m to 0.38 m.

Abstract Subcarrier modulated signals are widely used in the new generation of Global Navigation Satellite Systems (GNSS) to improve spectral compatibility and ranging accuracy. The unique structure of subcarrier modulated signals poses an ambiguity threat to acquisition and tracking, rendering traditional GNSS signal processing algorithms unsuitable. Designing unambiguous acquisition and tracking as well as observation extraction techniques for subcarrier modulated signals that can realize the high ranging accuracy potential while avoiding the ambiguity threat is a topic of common interest in both academia and industry. Numerous studies have addressed the ambiguity elimination problem of subcarrier modulated signals. However, to the best of our knowledge, no updated reviews on this topic reflecting recent advances in the field have been presented in the past few years. On the one hand, the unique composite subcarrier modulated signal structure in the B1 and B2 bands of the BeiDou Navigation Satellite System (BDS) and the E5 band of the Galileo satellite navigation system (Galileo) has been gradually emphasized by academics. On the other hand, unambiguous processing methods for subcarrier modulated signals based on multidimensional tracking loops have flourished, which provides a new way to solve the ambiguity problem. An overview of the most representative subcarrier modulated signal processing techniques, focusing on the achievements made in the last decade, is presented in this article, which can be a starting point for the researchers who start their study in this area. Furthermore, we aim to present a complete review on the recent breakthroughs that have taken place within this research area, providing links to the most interesting and successful advances in this research field. Remaining challenges with prospects are also given.

Abstract With the evolution of Global Navigation Satellite System (GNSS), new generation GNSS signals have adopted the dual-frequency multiplexing modulation techniques, which jointly modulate multiple signals located on multiple sub-frequencies into a Wideband Multiplexed Signal (WMS). Although WMSs were proposed initially to reduce the complexity of satellite transmitters and improve the transmission efficiency of signals, their multi-component structures and wide root mean square bandwidths introduced by high-frequency subcarriers also provide the possibility to improve the GNSS ranging precision. Therefore, this paper proposes a Dual-assisted Multi-component Tracking (DMT) technique, which can not only fully use high-frequency subcarriers in WMSs, but also effectively track carrier, subcarrier, and code by jointly utilizing all components in WMS. In this paper, the tracking and ranging performances of DMT are comprehensively analyzed theoretically and by simulation and real experiments. The results show that compared with existing WMS tracking methods, DMT can achieve tracking results with lower tracking jitters and ranging results with higher precision, providing a highly advantageous solution for new generation GNSS signal processing.

Advanced receiver autonomous integrity monitoring (ARAIM) is an integrity technique for a global navigation satellite system (GNSS), centered on the multiple hypothesis solution separation (MHSS) test, which assesses the consistency between a subset and the all-in-view solution. Successful fault exclusion (FE) in ARAIM relies on identifying exclusion candidates that ensure no faults among the remaining satellites, a process requiring computationally expensive MHSS tests. The existing methods guide exclusion candidate searches based on the size of the normalized solution separation statistics, i.e., the normalized absolute difference between the subset solution and the all-in-view solution. However, in scenarios involving more than one satellite fault, these statistics can become unreliable due to fault diversity and interactions, perhaps misleading the FE process and causing its failure. To overcome this issue, our study proposes employing sparse estimation to simply identify satellite faults in one go, leveraging the sparsity of faulty satellites compared to the total number of observations in civil aviation GNSSs. Unlike the existing methods, which infer the fault likelihood indirectly through solution separation statistics, our approach represents an improvement that directly indicates potential exclusion candidates. Our experiments demonstrate that this method is fast and accurate. As a fundamentally different approach, it serves as a valuable complement or an alternative to the existing methods, enhancing the success and efficiency of the ARAIM FE process.

Abstract Deploying an unmanned aerial vehicle (UAV) to scatter positioning beacons and further calibrate their positions by obtaining range measurements enables the establishment of a wireless positioning network in GNSS‐denied environments where hand placement is not an option. The influence of the UAV trajectory on the calibration quality is investigated and methods to find optimal waypoints at which the UAV makes observations are determined. A Fisher information matrix‐based metric is devised and two waypoint selection algorithms are proposed to optimise calibration quality, one‐off waypoint selection, and iterative and incremental waypoint selection. Numerical results show that optimised waypoints provide more information about beacon positions and therefore produce a more accurate wireless positioning network.

Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments.

Abstract In this paper, we propose a fully tightly‐coupled multi‐sensor fusion framework termed FT‐LVIO, that fuses measurements from a light detection and ranging (LiDAR), a monocular camera and an inertial measurement unit (IMU) simultaneously to achieve robust and accurate state estimation in real time. FT‐LVIO is built atop the framework of an error‐state‐iterated Kalman filter. To take full advantage of the complimentary characteristics of individual sensors, LiDAR point clouds are undistorted by IMU prediction to the nearest camera exposure time and the filter is updated with measurements from all sensors. In addition, an efficient sampling method for the LiDAR point‐to‐plane measurements is proposed, which can help select the measurements providing sufficient constraints to the pose estimation and facilitate a low‐drift odometry. Extensive experiments are performed on both the public NTU dataset and the private handheld dataset, and the results show that the proposed FT‐LVIO outperforms the state‐of‐the‐art LiDAR‐inertial, visual‐inertial and LiDAR‐visual‐inertial methods in both accuracy and robustness. Furthermore, FT‐LVIO can survive in the challenging staircase environment.

Abstract Multi‐path becomes a main source of positioning errors in new‐generation global navigation satellite systems (GNSS). Code multi‐correlator discriminators (MCD), represented by narrow early‐minus‐late (NEML) and double delta (DD) discriminators which are designed for binary phase offset keying (BPSK) signals, are one of the main multi‐path mitigation techniques. However, because of the complicated auto‐correlation function and intricate relationships among multiple correlators, it is hard to implement a trial and error approach, which is the conventional design method of discriminator structures, to specifically design code MCD structures for new GNSS signals which have complicated spreading waveforms. In this article, instead of a trial and error approach, a general design methodology for designing code MCD structures based on heuristic optimisation for multi‐path mitigation is proposed. The proposed method can specifically design code MCD structures for signals with various modulations and receivers with different bandwidths, indicating the adaptability of the proposed method. Multi‐path mitigation performances of such designed structures are better than those of traditional code discriminator structures with strong tracking robustness and slight degradation of thermal noise performance. Designed code MCD structures are also insensitive to multi‐path relative amplitudes and bandwidths of receivers, ensuring the practicality of the designed code MCD structures.

Signal quality monitoring (SQM) is a technique utilized by satellite- and ground-based augmentation systems (SBAS/GBAS) to detect potential hazardous deformations in signals and better protect integrity for safety-critical users. The next generation of SBASs will incorporate dual-frequency multi-constellation (DFMC) techniques, for which SQM is particularly important since signal deformations might be the largest source of uncertainty in ranging error after first-order ionospheric delays are eliminated. However, the performance bounds of the traditional multi-correlator-based SQM technique face some challenges because of the raised requirement on detection sensitivity by dual-frequency ionosphere-free measurements and multiple modulation modes of civilian signals from multi-constellation techniques. To mitigate the challenges and improve overall performance, SQM based on chip domain observables (CDOs) is emerging, but has not yet been systematically studied. We propose a design methodology for CDO-based SQM, consisting of derivations and corresponding massive simulations. Correctness and effectiveness are assessed to confirm the methodology, and a simplification process by checking the sensitivity of CDOs is demonstrated in terms of implementation.

Global navigation satellite systems (GNSS) are vulnerable to spoofing attacks. To shut down a spoofer, it is necessary to locate the spoofer first. Many spoofer localization systems use long cables for the synchronization of multiple receivers. However, a flexible spoofer localization system free from cables is sometimes essential so the receivers can move freely and are flexible to deploy. This paper solves two major problems in developing such a system: spoofing discrimination without requiring synchronization and having an effective method using asynchronous raw measurements with no other assistance. First, this paper proposes to use the extended pseudorange double-difference method to discriminate spoofing signals. The performance is then analyzed and the effectiveness is verified. Then, a quasi-synchronization spoofer localization method (QSSL) is proposed, and it is verified that its localization performance can attain the Cramer-Rao lower bound. Above all, a field experiment demonstrates the effectiveness of the proposed methods and the feasibility of such system.

Tracking ambiguity and multipath are two serious threats in processing binary offset carrier (BOC) signals that are widely used in global navigation satellite systems (GNSS). Three-loop tracking methods, such as dual binary phase-shift keying tracking (DBT), use a delay lock loop (DLL), a phase lock loop (PLL), and a subcarrier PLL (SPLL) to track the code, carrier, and subcarrier, respectively, thus solving the tracking ambiguity problem. However, the multipath remains an important factor in the ranging performance deterioration of these tracking methods. Using the offset correlator (OC) technique in the SPLL can effectively mitigate subcarrier multipath errors, but it substantially raises thermal noise errors. To solve this contradiction, this paper proposes a prompt-assisted-offset correlator (PAOC) technique that combines the prompt and offset correlators in a complementary way to mitigate multipath for BOC signals. Compared with the original OC technique, the PAOC technique has less thermal noise performance loss. Moreover, this paper discovers and quantifies the interaction between carrier and subcarrier multipath errors by analyzing the coupling effect between SPLL and PLL. Most multipath mitigation methods for BOC signals ignore the carrier multipath in the PLL, so their subcarrier multipath performance is not optimal. Thus, this paper further proposes applying the PAOC technique in both PLL and SPLL to mitigate carrier and subcarrier multipath errors simultaneously. Theoretical analysis and experimental results demonstrate that the proposed method can significantly improve the multipath performance with small noise performance loss.

To guarantee the integrity of a global navigation satellite system (GNSS) for safety-critical users, a satellite-based augmentation system (SBAS) makes use of the integrity monitoring architecture, of which the signal quality monitor (SQM) is an important component to address the potential risks caused by satellite-induced signal anomalies. Due to the introduction of dual-frequency multi-constellation (DFMC) techniques in 2025, the ranging uncertainty will be reduced by the elimination of first-order ionospheric delay, but the biases measured in each individual signal will be inflated by the ionosphere-free combinations. Moreover, multiple modulations of DFMC signals might introduce applicability uncertainty of a traditional SQM method that has been protecting GPS L1C/A signal only. Thus, higher requirements are put forward for future SQM methods in detection sensitivity and modulation independence. This paper first proposes a design methodology for the SQM algorithm for BDS B1C/B2a signals, which could be easily extended to the DF combinations of other GNSS core constellations. Then, by comparing the performances of SQM baseline algorithms based on traditional multi-correlator and emerging chip domain observables (CDOs), respectively, the superiority of CDO-based SQM is declared. Detailed design iterations are further discussed, including the algorithm practicalization with optimizing code-phase bin length and lowering sampling frequency, as well as the metric simplification, to promote the overall performance while preserving a lower implementation complexity. Ultimately, a CDO-based SQM algorithm for BDS B1C/B2a signals is reached, which would be considered as an effective candidate in new generation DFMC SBASs.

In the absence of GNSS or when signals from satellites are blocked in harsh environments, a ground-based positioning system can be used to estimate the position of users and receivers. Nevertheless, ground-based systems suffer dramatic nonlinear error resulting from the linearization used in typical positioning algorithms. Robust positioning algorithms that are capable of handling strong nonlinearity cases are therefore of great value. In this paper, we propose an algorithm termed promoted iterative least-squares based on nonlinear-compensation (PILSBON) to effectively alleviate the influence of nonlinear effects. This algorithm is based on the accurate expression of the nonlinear error terms in the double-differenced pseudorange measurement model. In order to eliminate the effects of nonlinearity for targeted solutions, the PILSBON uses iterative numerical estimation to compensate for nonlinear error so that the nonlinear position determination is transformed into a linear model, which can then be estimated with a linear estimation algorithm. In this paper, we analyze the properties of the PILSBON and compare it to conventional solutions. Because of its specific strategy for nonlinearity, our results show that the PILSBON improves the overall accuracy by approximately 30% compared with conventional solutions according to statistic RMSE data from over 500 experiments and positions. Moreover, by deploying a practical ground-based positioning system with six transmitters on the rooftop of our laboratory building, we demonstrate that the PILSBON algorithm can be efficiently employed in real-world experiments.

Abstract A receiver-spoofer is one of the most covert global navigation satellite system (GNSS) spoofing attacks and can only be effectively detected by the combination of multiple anti-spoofing technologies. In this paper, an analysis of influencing parameters for receiver-spoofers indicates that the ratio of the spoofing signal amplitude versus the authentic signal amplitude (spoofing-signal ratio) is a key parameter for spoofing results. For a spoofer to ensure covertness, the goal is to maintain a low spoofing-signal ratio. The carrier phase difference and code phase difference between authentic signals and spoofing signals resulted in errors in the position estimation of the target receiver increase the lower limit of the spoofing-signal ratio required for successful spoofing. A spoofing signal alters the phase of local replicate code based on the original balance of a receiver phase discriminator to seize control. Based on this principle, the lower limit of the spoofing-signal ratio that corresponds to various phase discriminator spacings, carrier phase differences, and code phase differences is deduced in this paper. Two tests are designed for the simulation source and authentic navigation signals to verify the deduced formula. The lower limit of the spoofing-signal ratio obtained from these tests matches the calculated results, which proves the validity and effectiveness of the derived algorithm.

Global navigation satellite systems are now threatened by spoofing attacks. The double-antenna countermeasure is one of the most robust spoofing detection techniques. However, the existing doubleantenna countermeasure is based on the assumption that the measurements are synchronized, which may result in failure if the assumption is not valid. In the paper, we propose an unsynchronized model for the pseudorange and carrier phase measurements and discuss why these measurements are not suitable for spoofing detection when the measurements are not synchronized. In addition, a new detection method based on double-antenna power measurements is proposed. This method can be used in the unsynchronized case, extending the application of the double-antenna-based spoofing countermeasure.

As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible solution to the issue. For prior-map-free situations, as the only sensor with a global frame, RTK determines and maintains the global positioning precision of the integrated system. However, RTK performance degrades greatly in GNSS-challenged environments, and most of the existing integrated systems adopt loose coupling mode, which does nothing to improve RTK and, thus, prevents integrated systems from further improvement. Aiming at improving RTK performance in the RTK/LiDAR/INS integrated system, we proposed an innovative integrated algorithm that utilizes RTK to register LiDAR features while integrating the pre-registered LiDAR features to RTK and adopts parallel filters in the ambiguity-position-joint domain to weaken the effects of low satellite availability, cycle slips, and multipath. By doing so, we can improve the RTK fix rate and stability in GNSS-challenged environments. The results of the theoretical analyses, simulation experiments, and a road test proved that the proposed method improved RTK performance in GNSS-challenged environments and, thus, guaranteed the global positioning precision of the whole system.

Security of global navigation satellite systems (GNSS) is important since the navigation capability provided by the GNSS is a key enabler for many civilian and military applications. Spoofing attacks threaten the GNSS security and have caught much attention recently. The spatial processing method is one of the most robust GNSS spoofing countermeasures, which detects spoofing signals with a moving antenna or multi-antenna, but it cannot work in a static single-antenna receiver. In this paper, we propose a spoofing countermeasure based on the power measurements of a single rotating antenna, which can be implemented in a static receiver. The method takes advantages of the anisotropy of the antenna's gain pattern to detect spoofing signals. When the antenna is rotating, the power measurements of the spoofing signals coming from the same direction change similarly and the correlation coefficients between them are close to 1, but the power measurements of the authentic signals are uncorrelated. Since it is not easy to evaluate the anti-spoofing performance of the correlation coefficient, another metric named phase difference of power measurements is proposed. Its theoretical performance is derived based on generalized likelihood ratio test and validated with simulations. Actual experiments indicate that both the simulated and meaconing spoofing signals can be distinguished from the authentic ones, and the method can be implemented in a static or low-dynamic conventional receiver, only with an additional low-cost rotary table.

Radio-based positioning systems are typically utilized to provide high-precision position information for automatic-guided vehicles (AGVs). However, the presence of obstacles in harsh environments, as well as carried cargoes on the AGV, will degrade the localization performance, since they block the propagation of radio signals. In this paper, a distributed multi-antenna positioning system is proposed, where multiple synchronous antennas are equipped on corners of an AGV to improve the availability and accuracy of positioning. An estimator based on the Levenberg−Marquardt algorithm is introduced to solve the nonlinear pseudo-range equations. To obtain the global optimal solutions, we propose a coarse estimator that utilizes the displacement knowledge of the antennas to provide a rough initial guess. Simulation results show a better availability of our system compared with the single antenna positioning system. Decimeter accuracy can be obtained under a Gaussian measurement noise with a standard deviation of 0.2 m. The results also demonstrate that the proposed algorithm can achieve positioning accuracy close to the theoretical Cramer−Rao lower bound. Furthermore, given prior information of the yaw angle, the same level of accuracy can be obtained by the proposed algorithm without the coarse estimation step.

The emerging Global Navigation Satellite Systems (GNSS) including the BeiDou Navigation Satellite System (BDS) offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK) algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.

Jamming and spoofing are the two main types of intentional interference for global navigation satellite system (GNSS) receivers. Due to the entirely different signal characteristics they have, a few techniques can deal with them simultaneously. This paper proposes a two-stage interference suppression scheme based on antenna arrays, which can detect and mitigate jamming and spoofing before the despreading of GNSS receivers. First, a subspace projection was adopted to eliminate the high-power jamming signals. The output signal is still a multi-dimensional vector so that the spatial processing technique can be used in the next stage. Then, the cyclostationarity of GNSS signals were fully excavated to reduce or even remove the noise component in the spatial correlation matrix. Thus, the signal subspace, including information of the power and the directions-of-arrival (DOAs) of the GNSS signals, can be obtained. Next, a novel cyclic correlation eigenvalue test (CCET) algorithm was proposed to detect the presence of a spoofing attack, and the cyclic music signal classification (Cyclic MUSIC) algorithm was employed to estimate the DOAs of all the navigation signals. Finally, this study employed a subspace projection again to eliminate the spoofing signals and provide a higher gain for authentic satellite signals through beamforming. All the operations were performed on the raw digital baseband signal so that they did not introduce additional computational complexity to the GNSS receiver. The simulation results show that the proposed scheme not only suppresses jamming and spoofing effectively but also maximizes the power of the authentic signals. Nonetheless, the estimated DOA of spoofing signals may be helpful for the interference source positioning in some applications.

Robust and centimeter-level Real-time Kinematic (RTK)-based Global Navigation Satellite System (GNSS) positioning is of paramount importance for emerging GNSS applications, such as drones and automobile systems. However, the performance of conventional single-rover RTK degrades greatly in urban environments due to signal blockage and strong multipath. The increasing use of multiple-antenna/rover configurations for attitude determination in the above precise positioning applications, just as well, allows more information involved to improve RTK positioning performance in urban areas. This paper proposes a dual-antenna constraint RTK algorithm, which combines GNSS measurements of both antennas by making use of the geometric constraint between them. By doing this, the reception diversity between two antennas can be taken advantage of to improve the availability and geometric distribution of GNSS satellites, and what is more, the redundant measurements from a second antenna help to weaken the multipath effect on the first antenna. Particularly, an Ambiguity Dilution of Precision (ADOP)-based analysis is carried out to explore the intrinsic model strength for ambiguity resolution (AR) with different kinds of constraints. Based on the results, a Dual-Antenna with baseline VEctor Constraint algorithm (RTK) is developed. The primary advantages of the reported method include: (1) Improved availability and success rate of RTK, even if neither of the two single-antenna receivers can successfully solve the AR problem; and (2) reduced computational burden by adopting the concept of measurement projection. Simulated and real data experiments are performed to demonstrate robustness and precision of the algorithm in GNSS-challenged environments.

Global navigation systems provide worldwide positioning, navigation and navigation services. However, in some challenging environments, especially when the satellite is blocked, the performance of GNSS is seriously degraded or even unavailable. Ground based positioning systems, including pseudolites and Locata, have shown their potentials in centimeter-level positioning accuracy using carrier phase measurements. Ambiguity resolution (AR) is a key issue for such high precision positioning. Current methods for the ground based systems need code measurements for initialization and/or approximating linearization. If the code measurements show relatively large errors, current methods might suffer from convergence difficulties in ground based positioning. In this paper, the concept of double-differential square observation (DDS) is proposed, and an on-the-fly ambiguity resolution (OTF-AR) method is developed for ground based navigation systems using two-way measurements. An important advantage of the proposed method is that only the carrier phase measurements are used, and code measurements are not necessary. The clock error is canceled out by two-way measurements between the rover and the base stations. The squared observations are then differenced between different rover positions and different base stations, and a linear model is then obtained. The floating integer values are easy to compute via this model, and there is no need to do approximate linearization. In this procedure, the rover’s approximate coordinates are also directly obtained from the carrier measurements, therefore code measurements are not necessary. As an OTF-AR method, the proposed method relies on geometric changes caused by the rover’s motion. As shown by the simulations, the geometric diversity of observations is the key factor for the AR success rate. Moreover, the fine floating solutions given by our method also have a fairly good accuracy, which is valuable when fixed solutions are not reliable. A real experiment is conducted to validate the proposed method. The results show that the fixed solution could achieve centimeter-level accuracy.

Vehicles driving in urban canyons are always confronted with a degraded Global Navigation Satellite System (GNSS) signal environment. The surrounding obstacles may cause signal reflections or blockages, which lead to large multipath noises and intermittent GNSS reception. Under these circumstances, it is not feasible to use conventional real-time kinematic (RTK) algorithms to maintain high-precision performance for positioning. In order to meet the special requirements of safety-critical applications under non-ideal observation conditions, a novel tightly coupled RTK/Inertial Navigation System (INS) algorithm is proposed in this paper, which can provide accurate and reliable positioning results continuously. Our integrated RTK/INS algorithm has three features. Firstly, INS measurements are used to help search for integer ambiguities in the position domain. INS solutions can provide a more accurate initial location and a more efficient search region. Secondly, the criterion for determining whether a candidate position is the correct solution is only related to the fractional value of the carrier-phase measurement. Thus, the new algorithm is immune to cycle slips as well as large pseudorange noises. Thirdly, our algorithm can provide more accurate ranging information than the pseudorange, even though it may not necessarily be fixed successfully, because it selects the weighted ambiguity solution as the result rather than the candidate point with maximum probability. The proposed algorithm is demonstrated on both simulated and real datasets. Compared with single epoch RTK and conventional tightly coupled RTK/INS integrations that search integer ambiguities in the ambiguity domain, our method attains better accuracy and stability in a simulated environment. Moreover, the real experimental results are presented to validate the performance of the new integrated navigation algorithm.

Traditional attitude determination using global navigation satellite system (GNSS) carrier phases is mostly applied on geodetic-grade receivers with sufficient baseline length. However, for some special applications such as mobile communication base station smart antenna attitude determination, only low-cost receivers with ultra-short baselines can be employed, and the environments are more challenging. When solving the ambiguity resolution (AR) problem with low-cost receivers, it is hard for the traditional methods in ambiguity domain to estimate float ambiguities accurately due to the large code pseudorange noises; thus, such systems fail to determine the correct ambiguities. Aiming at improving the AR success rate for ultra-short baselines attitude determination with low-cost receivers, we provide an objective function named Mean Square Residual (MSR) based on the geometrical relationship among the position spherical search space, the fractional carrier phases, and the possible ambiguities. The method can be calculated without code pseudoranges, and thus, can provide a higher AR success rate when using low-cost receivers. The corresponding analysis and acceptance test method are discussed in this contribution, and further, as an extension for more complicated urban dynamic applications, a GNSS/Inertial Navigation System (INS) integrated system is introduced. Several experiments have been conducted to verify performance.

Along with the rapid development of the Global Navigation Satellite System (GNSS), satellite navigation signals have become more diversified, complex, and agile in adapting to increasing market demands. Various techniques have been developed for processing multiple navigation signals to achieve better performance in terms of accuracy, sensitivity, and robustness. This paper focuses on a technique for processing two signals with separate but adjacent center frequencies, such as B1I and B1C signals in the BeiDou global system. The two signals may differ in modulation scheme, power, and initial phase relation and can be processed independently by user receivers; however, the propagation delays of the two signals from a satellite are nearly identical as they are modulated on adjacent frequencies, share the same reference clock, and undergo nearly identical propagation paths to the receiver, resulting in strong coherence between the two signals. Joint processing of these signals can achieve optimal measurement performance due to the increased Gabor bandwidth and power. In this paper, we propose a universal scheme of asymmetric dual-band tracking (ASYM-DBT) to take advantage of the strong coherence, the increased Gabor bandwidth, and power of the two signals in achieving much-reduced thermal noise and more accurate ranging results when compared with the traditional single-band algorithm.

It is well known that multipath effect remains a dominant error source that affects the positioning accuracy of Global Navigation Satellite System (GNSS) receivers. Significant efforts have been made by researchers and receiver manufacturers to mitigate multipath error in the past decades. Recently, a multipath mitigation technique using dual-polarization antennas has become a research hotspot for it provides another degree of freedom to distinguish the line-of-sight (LOS) signal from the LOS and multipath composite signal without extensively increasing the complexity of the receiver. Numbers of multipath mitigation techniques using dual-polarization antennas have been proposed and all of them report performance improvement over the single-polarization methods. However, due to the unpredictability of multipath, multipath mitigation techniques based on dual-polarization are not always effective while few studies discuss the condition under which the multipath mitigation using a dual-polarization antenna can outperform that using a single-polarization antenna, which is a fundamental question for dual-polarization multipath mitigation (DPMM) and the design of multipath mitigation algorithms. In this paper we analyze the characteristics of the signal received by a dual-polarization antenna and use the maximum likelihood estimation (MLE) to assess the theoretical performance of DPMM in different received signal cases. Based on the assessment we answer this fundamental question and find the dual-polarization antenna’s capability in mitigating short delay multipath—the most challenging one among all types of multipath for the majority of the multipath mitigation techniques. Considering these effective conditions, we propose a dual-polarization sequential iterative maximum likelihood estimation (DP-SIMLE) algorithm for DPMM. The simulation results verify our theory and show superior performance of the proposed DP-SIMLE algorithm over the traditional one using only an RHCP antenna.

The spoofing attack is one of the security threats of systems depending on the Global Navigation Satellite System (GNSS). There have been many GNSS spoofing detection methods, and each of them focuses on a characteristic of the GNSS signal or a measurement that the receiver has obtained. The method based on a single detector is insufficient against spoofing attacks in some scenarios. How to fuse multiple detections together is a problem that concerns the performance of GNSS anti-spoofing. Scholars have put forward a model to fuse different detection results based on the Dempster-Shafer theory (DST) of evidence combination. However, there are some problems in the application. The main challenge is the valuation of the belief function, which is a key issue in DST. This paper proposes a practical method of detections’ fusion based on an approach to assign the belief function for spoofing detections. The frame of discernment is simplified, and the hard decision of hypothesis testing is replaced by the soft decision; then, the belief functions for some detections can be evaluated. The method is discussed in detail, and a performance evaluation is provided, as well. Detections’ fusion reduces false alarms of detection and makes the result more reliable. Experimental results based on public test datasets demonstrate the performance of the proposed method.

In order to provide better navigation service for a wide range of applications, modernized global navigation satellite systems (GNSS) employs increasingly advanced and complicated techniques in modulation and multiplexing of signals. This trend correspondingly increases the complexity of signal despreading at the receiver when matched receiving is used. Considering the numerous low-end receiver who can hardly afford such receiving complexity, it is feasible to apply some receiving strategies, which uses simplified forms of local despreading signals, which is termed unmatched despreading. However, the mismatch between local signal and received signal causes performance loss in code tracking, which is necessary to be considered in the theoretical evaluation methods of signals. In this context, we generalize the theoretical signal evaluation model for unmatched receiving. Then, a series of evaluation criteria are proposed, which are decoupled from unrelated influencing factors and concentrates on the key factors related to the signal and its receiving, thus better revealing the inherent performance of signals. The proposed evaluation criteria are used to study two GNSS signals, from which constructive guidance are derived for receivers and signal designer.

For outdoor and global navigation satellite system (GNSS) challenged scenarios, collaborative positioning algorithms are proposed to fuse information from GNSS satellites and terrestrial wireless systems. This paper derives the Cramer-Rao lower bound (CRLB) and algorithms for the angle-of-arrival (AOA)-assisted GNSS collaborative positioning. Based on the CRLB model and collaborative positioning algorithms, theoretical analysis are performed to specify the effects of various factors on the accuracy of collaborative positioning, including the number of users, their distribution and AOA measurements accuracy. Besides, the influences of the relative location of the collaborative users are also discussed in order to choose appropriate neighboring users, which is in favor of reducing computational complexity. Simulations and actual experiment are carried out with several GNSS receivers in different scenarios, and the results are consistent with theoretical analysis.

Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.