Rapid Initial Heading Alignment for MEMS Land Vehicular GNSS/INS Navigation System

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.

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.

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) 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.

Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009

Now a days, Unmanned Aerial Vehicles (UAVs) have been exploited in numerous civil and military aspects such as disaster management and border monitoring. Although most of these UAVs are equipped with Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) reliable integrated system for localization proposes, they are susceptible to lose their GNSS signals under some environmental and artificial challenging circumstances such as, flying in urban areas, jamming, and spoofing. In such complicated scenarios, the navigation solution will degrade rapidly due to the accumulated INS drift errors. Although various aiding sensors such as cameras, and radar are employed to mitigate the INS drift errors, their estimated positioning accuracy is still affected by some factors. This paper presents a nested optimal filter approach (G-H filter, Extended Kalman Filter (EKF)) for UAVs in GNSS denied environment, to enhance the estimated positioning accuracy by integrating the Radar Odometry (RO)/INS/MAG to sustain a reliable navigation solution during GNSS outage. First, the UAV relative velocity is calculated based on image processing approach from the radar range doppler map, then the G-H filter is deployed to improve the accuracy of the vehicle estimated forward velocities based on the vehicle dynamic characteristics to reduce the effect of unrealistic velocity estimate from the radar. These improved velocities are then fused with the Inertial Measurement Unit (IMU), magnetometer, and barometer via EKF to mitigate the positioning RMS errors during the GNSS signals outages. The experimental results demonstrate the proposed system's ability to notably enhance the 3D positioning accuracy during the GNSS signal outage by more than 99% when compared to a stand-alone IMU dead reckoning solution.

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.

Under the high dynamic conditions, Global Navigation Satellite System (GNSS) signals produce great Doppler frequency shifts, which hinders the fast acquisition of signals. Inertial Navigation System (INS)-aided acquisition can improve the acquisition performance, whereas the accuracy of Doppler shift and code phase estimation are mainly determined by the INS precision. The relation between the INS accuracy and Doppler shift estimation error has been derived, while the relation between the INS accuracy and code phase estimation error has not been deduced. In this paper, in order to theoretically analyze the effects of INS errors on the performance of Doppler shift and code phase estimations, the connections between them are re-deduced. Moreover, the curves of the corresponding relations are given for the first time. Then, in order to have a better verification of the INS-aided acquisition, a high dynamic scenario is designed. Furthermore, by using the deduced mathematical relation, the effects of different grade INS on the GNSS (including Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)) signal acquisition are analyzed. Experimental results demonstrate that the INS-aided acquisition can reduce the search range of local frequency and code phase, and achieve fast acquisition. According to the experimental results, a suitable INS can be chosen for the deeply coupled integration.

Conventional global navigation satellite system (GNSS)/micro-electromechanical strapdown inertial navigation system (MEMS-SINS) tight integration encounters some major limitations in GNSS-challenged areas. It is GNSS signal impairments due to the complex environment surrounding GNSS antenna, that plagues the acquired integration performance, resulting in significant accuracy degradation of MEMS-based inertial navigation. To improve the overall performance of GNSS/MEMS-SINS tightly-coupled (TC) system, this paper proposes the following three-tier approach at different levels without either additional sensors or GNSS base stations: 1) improving the stochastic modeling for both MEMS sensors and GNSS receiver clock errors using Allan variance (AV) analysis and satellite-differencing (SD) at the dynamics level, 2) enhancing the integration by augmenting unambiguous time-differenced carrier phase (TDCP) into Kalman filter at the measurement level, and 3) eliminating the adverse effects of GNSS outliers by devising innovation-based test statistics at the information fusion level. The field test results indicate that the proposed algorithm could improve the overall navigation accuracy by about 42.14% (position), 38.96% (velocity) and 33.56% (attitude) with immunity to both GNSS outliers and cycle slips, compared with conventional TC counterpart. Moreover, it can also significantly reduce the coasting errors and improve the relative position accuracy during relatively long outages. The results demonstrate the effectiveness of the proposed algorithm in reducing the growth of MEMS-SINS error.

Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) are the most commonly used navigation systems. They both have unique advantages and disadvantages. GNSS is capable of generating precise navigation solutions while enough satellites are in view. However, the GNSS signals are sensitive to the environment. While the signals is attenuated, the GNSS receiver will fail to provide reliable navigation solutions. INS is an advanced navigation system built based on Newton's law. Due to the random noises contained in the Inertial Measurement Unit (IMU), the INS navigation solutions errors diverge over time. Therefore, effective integration of the two common systems can obtain better navigation results than any individual system. In this paper, for enhancing the GNSS/INS tightly integration system with low cost, multiple receivers were employed in the tight integration. Pseudo-range and Pseudo-range rates from the multiple receivers were employed to compose the measurement vector of the integration filter. In order to reduce the computation load, a measurement difference method was proposed. The state vector dimension could be reduced with the measurement difference scheme. Both simulation and field test were carried out for evaluating the performance of the proposed method. Since it was hard to obtain GNSS raw measurements from commercial receivers, self-developed DSP+FPGA (Digital Signal Processor, DSP; Field Programmable Gate Array, FPGA) based GNSS receivers were employed in the field test. The statistical analysis of the results showed that the positioning errors decreased with the receiver's amount increasing. In addition, a measurement difference method was proposed to reduce the state vector dimension for saving the computation load with the identical navigation solutions accuracy.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

Abstract. Multisensor systems are essential for autonomous navigation applications to achieve reliable accuracy. Integrating the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) is the most common integration scheme. However, this integration is unreliable in different scenarios since the GNSS signal may deteriorate in downtown areas or suffer from a blockage in underground and indoor areas. Therefore, other sensors are integrated with INS to compensate for GNSS outages. This paper proposes a novel algorithm for radar/INS tightly-coupled integration for autonomous navigation applications. This algorithm is applied in multiple steps. Radar data analysis is the first and most crucial step to remove the noisy data and the outliers and keep the useful objects. Then, data association is done to match the detected objects between radar frames. The tightly-coupled integration is performed at the measurement level through an Extended Kalman Filter (EKF), where the distance between the INS and the detected objects can be predicted from the INS and measured from the radar. Real data was collected from four Frequency Modulated Continuous Wave (FMCW) radar units in Calgary's suburban areas and Toronto's downtown area. The proposed algorithm was tested and assessed by introducing simulated GNSS single outages with different durations. The results show an enhancement in the vehicle's position by about 94% to 96%.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

In the last two decades, the demand for geo-spatial data has tremendously increased. Mobile mapping Systems (MMS) are considered the main source of data for mapping and Geographic Information Systems (GIS) applications. MMS, which integrates navigation systems (such as Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS)) and imaging sensors (such as cameras and laser scanners), have been an emerging trend in mapping applications because they allow the implementation of the mapping concepts from moving vehicles (e.g. cars, airplanes and trains). The main strength of MMS which makes it desirable for different mapping applications is their ability to directly georeference their mapping sensors, which means knowing their position and orientation relative to a mapping co-ordinate frame. Once geo-referenced, the mapping sensor can be used to determine the positions of points external to the platform in the same mapping co-ordinate frame. Nevertheless, the major drawback of current MMS systems is their high price due to the type of the used platform, navigation and mapping technologies. Today's smartphones are getting more sophisticated and smarter and therefore they will narrow the gap between computers and mobile phones. Current generation of smartphones are equipped with GNSS receivers, micro-electro mechanical systems (MEMS) based navigation sensors (such as gyroscopes, accelerometers, magnetometers, and barometers) and high resolution digital cameras. As a result, smartphones are now getting quite popular for data collection projects including point data mapping tasks and it is not far before we see smartphones are used as mobile mappers. This paper will present a new and innovative application of a portable MMS based on smartphones that overcome the high-cost drawback of the current land-based MMS with reasonable accuracy.

This paper investigates into the feasibility of a combined use of Ultra-wideband (UWB) and Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) for integrated navigation in GNSS challenged environments. The performance of the original integrated GNSS/INS navigation system deteriorated rapidly due to the GNSS signal outages. The position information provided by UWB is used as the observation quantity of Extend Kalman Filter (EKF) to make up for the influence of GNSS signal loss. Considering the positioning service performance provided by UWB is closely related to the geometric distribution of base station of UWB, an optimization searching based on maximum convex-hull (OSMC) method is adopted to establish the optimal base station layout scheme. The features of navigation have been investigated in vehicle test with the results showing clear evidence that the method can resist the impact of GNSS outages effectively and the positioning information are stable and reliable.