Robust GNSS/INS Tightly-Coupled System with Time-Differenced Carrier Phase for Accurate MEMS-Based Navigation

Accuracy assessment of integrated GNSS measurements with LIDAR mobile mapping data in urban environments

A novel method of ambiguity resolution and cycle slip processing for single-frequency GNSS/INS tightly coupled integration system

Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) integration requires accurate modelling of both INS deterministic and stochastic errors. The Allan Variance (AV) analysis on INS static data is one method of determining INS stochastic errors. However, it is known that INS errors can vary depending on a vehicle’s motion and environment, and application of AV results from static data in kinematic operations typically results in an over-confident estimation of stochastic. In order to overcome this limitation, this paper proposes the use of Dynamic Allan Variance (DAV). The paper compares the resulting performance of the INS/GNSS integrated system by varying the stochastic coefficients obtained from the AV and DAV. The results show that the performance improved when utilizing the stochastic coefficients obtained from the DAV, applied on a kinematic dataset compared to the AV, applied on a static laboratory dataset.

The performance of the Global Navigation Satellite System (GNSS)-based navigation is usually degraded in challenging environments such as deep urban and light indoors. In such environments, the satellite visibility is reduced, and the complex propagation conditions perturb the GNSS signals with attenuation, refraction, and frequent reflection. This paper presents a novel Artificial Intelligence (AI)-based approach, to tackle the complex GNSS positioning problems in deep urban, even light indoors. The new approach, called LIGHT, i.e., Light Indoor GNSS macHine-learning-based Time difference carrier phase, can select healthy GNSS carrier phase data for positioning thanks to Machine Learning (ML). The selected carrier phase data are fed into a Time Difference Carrier Phase (TDCP)-based Extended Kalman Filter (EKF) to estimate the user's velocity. Four trajectories including shopping mall, railway station, shipyard, as well as urban canyon scenarios over a 3.2 km total walking distance with a handheld device are tested. It is shown that at least half of the epochs are selected as usable for light indoor GNSS TDCP standalone positioning, and the accuracy of the velocity estimates can improve up to 88% in terms of the 75 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{th}$</tex-math></inline-formula> percentile of the absolute horizontal velocity error compared with the state-of-the-art non-ML approach. Furthermore, a newly designed hybridization filter LIGHT-PDR that fuses the LIGHT algorithm and Pedestrian Dead Reckoning (PDR) solution is applied to perform seamless indoor/outdoor positioning in a more robust manner.

It is well known that the presence of unintentional or intentional Radio Frequency Interference (RFI) signals in the Global Navigation Satellite Systems (GNSS) frequency bands can cause severe positioning performance degradation and even a complete service unavailability. Moreover, with the development of Software Defined Radio (SDR) GNSS signal generators and the popularization of low-cost SDR front-ends, the spoofing of the GNSS civil service turns out to be a real threat for GNSS receivers. Complementary to time and frequency-domain mit-igation techniques, antenna-array based receivers can benefit from spatial domain processing. An antenna array receiver can also discriminate between legitimate GNSS signals from these being broadcasted by the spoofer. In this work, we present an evolution of a receiver-independent GNSS smart antenna architecture for a real-time, automatic and autonomous, simultaneous anti-jamming and anti-spoofing spatial filtering for GNSS bands. The novel spoofer detection and mitigation algorithm is able to detect the spoofer and protect the receiver even in a scenario where the spoofer is transmitted in combination with a strong jammer to force the receiver to lose the legitimate GNSS signal lock. A prototype is also presented, implemented using Commercial Off The Shelf (COTS) components. The proposed smart antenna can be connected to any conventional single-antenna GNSS receiver. The paper includes both the theory of operation, the implementation details and its performance analysis in an extended measurement campaign.

Global Navigation Satellite Systems (GNSS) has been the primary positioning solution for Unmanned Aerial Vehicles (UAVs) due to their worldwide coverage, high precision and lightweight receivers. However, GNSS is prone to electromagnetic interference and malicious assaults, including jamming or spoofing because of its low signal-to-noise ratio (SNR). To ensure the continuity and protection of UAV operations, using redundant navigation systems is essential. In recent years, the phased array radio system (PARS) has established itself as a local navigation solution. PARS is robust towards malicious assaults because of an much higher SNR than GNSS regarding directed and encrypted transmission. An essential factor of PARS is that the orientation of the radio antenna at a ground station needs to be precisely determined to obtain the correct positioning of UAVs. This paper presents a method for extending a previously proposed calibration algorithm to estimate the ground antenna orientation with an inertial navigation system (INS) aided by redundant positioning sensors (GNSS, PARS or barometer) using a multiplicative extended Kalman filter (MEKF) so that the calibration can be activated during flights whenever GNSS is available. In other words, the proposed navigation system is essentially an aided-INS which switches between two modes depending on the availability of GNSS: calibration and GNSS aiding mode when GNSS is available (Mode 1) and PARS and barometer aiding mode when GNSS is unavailable (Mode 2). Considering that the navigation system needs to include the effect of Earth’s curvature for a long-distance flight, PARS horizontal measurement and the barometer measurement were treated independently, and the navigation equations were propagated in Earth Centred Earth Fixed (ECEF) frame. The independent treatment of barometer measurement, and the propagation in ECEF frame were also beneficial when using multiple ground antennas to have a common reference point and reference frame. The proposed method was validated using data (Inertial Measurement Unit (IMU), GNSS, PARS, Pixhawk autopilot (including barometer) measurements) collected during a field test. In the validation, GNSS was made available at the middle of the flight and the calibration mode was activated for 200s. The proposed navigation system successfully estimated the precise orientation of multiple ground antennas and the navigation solutions were verified using GNSS and Pixhawk autopilot solutions as ground truth.

As the Global Satellite Navigation Systems (GNSS) signals are received and processed by the GNSS receivers with an extremely low power, they are vulnerable to non-intentional and intentional interference. In particular, the intentional transmission of Radio Frequency interference, known as jamming, can completely prevent the operation of GNSS receivers. The integration of measurements from multiple sensors with complementary error characteristics offers a possible countermeasure against jamming and, therefore, it can provide a position solution that has sufficient accuracy for the applications. This paper shows how a Deeply Coupled (DC) integration of GNSS with Inertial Navigation Systems (INS) and visual sensors enhances the robustness of the navigation system in presence of jamming. In particular, the effect of DC integration for jamming mitigation is investigated for a dynamic scenario where jamming signals have been added to a pre-recorded GNSS scenario by using the record and replay approach, emulating different interference levels. The results presented in this paper show that the deep coupling of GNSS with INS and visual sensors can sustain the navigation in presence of high power jamming, although such a strong interference completely masks the GNSS signal denying the navigation of a GNSS stand-alone system. Moreover, the test procedures suitability of the record and replay approach for the jamming scenario generation and combination with real datasets are discussed and analyzed.

Global navigation satellite systems (GNSS) products are integral to a wide array of scientific and commercial applications such as pecise orbit determination of low Earth orbit satellites, earthquake monitoring, GNSS reflectomrety, tropospheric and ionospheric research, surveying and much more. These products consisting of GNSS orbit, clock, phase biases and more are generated by the analysis centres of the International GNSS Service (IGS) by processing observations from a global network of ground stations to one or more GNSS constellations. The processing consists of a combined station position and GNSS satellite orbit determination through a least squares approach donated as global multi-GNSS processing.Within global multi-GNSS processing it is assumed that the observation noise is elevation-dependent and any spatial and temporal correlations are disregarded. Within numerous studies it has been shown that this assumption is incorrect while several studies additional pointed out that a sophisticated stochastic modelling has a positiv impact on GNSS processing and resulting products. In past reseach we have shown to exploit the post-fit residuals to derive temporal correlations for a sophisticated stochastic modeling. However, there have not been any large-scale investigations regarding the impact of stochastic modelling of observation noise on global GNSS processing products. Furthermore, to guarantee the quality of the GNSS products global multi-GNSS processing requires a sophisticated cycle slip detection and repairing algorithm. Cycle slips are discontinuities in the phase observations and if not corrected can lead to degrading quality of GNSS products. &amp;#160;&amp;#160;We present our advancements in global multi-GNSS processing by exploiting post-fit residuals for stochastic modeling and cycle slip detection. We used several years of observations and a selected IGS network of ground stations to generate GNSS products. Based on this data we analysed the impact our newly integrated approaches have on GNSS products such as orbits, clocks, phase biases and station coordinate time series. &amp;#160;

In this paper, we develop a novel method to detect Global Navigation Satellite Systems (GNSS) spoofing for an Automatic Dependent Surveillance-Broadcast (ADS-B) equipped aircraft. The Federal Aviation Administration (FAA) has mandated [18] all civil aircraft to be ADS-B Out equipped by January 1, 2020. The ADS-B Out broadcast sent to Air Traffic Control (ATC) consists of the aircraft's position, velocity, and other aircraft-specific information, all of which being unencrypted, poses a serious integrity threat. With readily available ADS-B trackers [19], [20], a spoofer can accurately track an aircraft to generate a spoofed trajectory that can go undetected [11]. We propose a novel method to modulate the ADS-B Out position broadcast such that a spoofed trajectory generated using the modulated ADS-B will be detectable by comparing Inertial Navigation System (INS) positions against those obtained using the spoofed GNSS signal. The amplitude of ADS-B modulation is selected to exceed the nominal INS error covariance so that spoofing is observable. In this work, we analytically quantify the magnitude of ADS-B modulation that will be sufficient for spoofing detection. During scenarios of GNSS signal jamming and spoofing, reauthentication of a reacquired GNSS signal is necessary to maintain integrity. During a GNSS outage, given enough time, the INS solution drift will grow large enough such that the spoofed GNSS solution might be within the INS error covariance envelope and go undetected. For GNSS outage scenarios we propose continuous modulation of ADS-B position and analytically quantify the magnitude such that spoofing of the GNSS signal is detectable after signal is re-acquired.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

On-board cooperative spacecraft relative navigation fusing GNSS with vision

With the rapid development of high-speed trains in the world, how to quickly collect train operation data becomes a significant issue. The paper uses Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and Driver Machine Interface (DMI) information fusion to obtain the position and speed information of the train more accurately. Among these information sources, GNSS and INS can carry out the collection of train position and speed information with and without obstructions, respectively. When the train is running in the open and unobstructed, GNSS can directly obtain train operation information. When the train passes through an environment with obstructions, GNSS and INS work simultaneously to obtain train operation information. The DMI on the train has the timely speed and kilometer mark information of the train. During the measurement, as we can not access to the onboard equipment. The camera is applied to collect the DMI interface. The image is processed to obtain the train data information. In this paper, the train data collected by GNSS/INS equipment is corrected by the Kalman filtering method. The data collected by DMI is filtered by the kinematics theorem and the least square method. What is more, a centralized fusion structure is used to process the DMI, GNSS, and INS data. This result can provide more accurate train time, speed, and kilometer mark information for the train during the operation test.

Abstract. Typically, in situations where Global Navigation Satellite System (GNSS) signals are unavailable, navigation systems rely on integrating GNSS and inertial navigation system (INS) data. While such integration can provide accurate positioning during short GNSS signal outages, it cannot sustain prolonged GNSS outages. The reason for this is that the system's performance depends solely on the INS and can result in significant errors over time. To address this issue, additional onboard sensors are necessary. This study proposes a navigation system that integrates INS and LiDAR simultaneous localization and mapping (SLAM) using an extended Kalman filter (EKF). The system was tested using the raw KITTI dataset in various outdoor driving scenarios without GNSS signals. It is shown that the proposed system significantly outperformed the INS-only system, with an average RMSE improvement of around 93% and 58% in the horizontal and the up directions, respectively.

Ship navigation requires accurate positioning, navigation and timing (PNT) data. PNT data from a single source has uncertainties and potential risks. Wrong PNT data has a huge impact on ship maneuvering, and at the same time, it may cause huge losses to national assets and national security. This chapter proposes a data fusion algorithm based on single-frequency global navigation satellite system (GNSS) and inertial navigation system (INS) to obtain PNT data, which can improve the availability, accuracy, reliability, continuity, and robustness. The experimental results show that the data fusion method combining median filtering and Kalman filtering can improve the system's ability to acquire PNT data. When blocking GNSS acquisition of PNT data, relying on INS, you can still obtain PNT data, which can make up for the ability to obtain PNT data from GNSS.

Cycle slips are discontinuity events in a receiver’s phase lock on a Global Navigation Satellite System (GNSS) signal. If cycle-slip events are not detected and repaired, the quality of positioning using carrier phase processing suffers. In this paper, we present an algorithm for flagging cycle slips during data preprocessing. This new algorithm is part of a new GNSS software that replaces a legacy software at the National Geodetic Survey (NGS). It uses the Hatch–Melbourne–Wübbena (HMW), ionosphere-free and geometry-free linear combinations to detect cycle slips. The use of the three observables in parallel helps improve sensitivity to cycle slips and overcome the challenge of detecting cycle slips of equal size and same sign on both/multiple frequencies. Different detection threshold values were evaluated in a well-controlled environment using GNSS data acquired from continuously operating reference stations (CORS) networks and artificially simulated cycle slips that were introduced into the datasets. The goal of evaluation was to determine the optimal values for cycle-slip detection parameters. In the absence of formal confidence limits, the results of the study show that, in a worst-case scenario involving small slips of magnitudes ranging from 1 to 3 cycles, there is a range of parameter values that resulted in at least 97% success rate without any false detections. Based on these and other findings, we provide a set of optimal values from which the results are an improvement of at least 34% in cycle-slip detection, compared to results from the legacy algorithm’s settings. The results of the study will support the National Oceanic and Atmospheric Administration’s (NOAA) GNSS services and tools for the public, such as the Online Positioning User Service (OPUS).