Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Integrity monitoring (IM) in autonomous navigation has been extensively researched, but currently available solutions are mainly applicable to specific algorithms and sensors, or limited by linearity or 'Gaussianity' assumptions. This study investigates a Solution Separation (SS) based framework for universal IM, scalable to multi-sensor fusion as each hypothesis assumes a whole sensor measurement set as faulty. Architecturally we consider that: 1) multi sensor systems must account for various sensor noise models which lead to inconsistent estimates of uncertainties, 2) a module must be able to detect sensor failure or sensor noise mismodeling and suggest better bounds for the error, without being constantly conservative, 3) some algorithms are computationally heavy to monitor in the SS setting or the provided covariances cannot be interpreted in IM. A hybrid SS architecture can be practical, where some solutions are evaluated with a navigation algorithm with known characteristics, although the all-sensor-in solution is evaluated with the monitored algorithm. Experiments are run on filter and smoothing-based navigation algorithms. In addition, we experiment with hybrid SS monitoring and time-correlated noise to evaluate the appropriability of our framework in the context of the above-mentioned requirements. This is a novel framework in the IM domain, directly integrable in existing navigation solutions and, in our opinion, it will facilitate the quantification of the effect of different sensors in navigation safety.publishedVersio

The Syncline Model -- Analyzing the Impact of Time Synchronization in Sensor Fusion

The accuracy of sensor fusion algorithms are limited by either the intrinsic sensor noise, or by the quality of time synchronization of the sensors. While the intrinsic sensor noise only depends on the respective sensors, the error induced by quality of, or lack of, synchronization depends on the dynamics of the vehicles and robotic system and the magnitude of time synchronization errors. To meet their sensor fusion requirements, system designers must consider both which sensor to use and also how to synchronize them. This paper presents the Syncline model, a simple visual model of how time synchronization affects the accuracy of sensor fusion for different mobile robot platform. The model can serve as a simple tool to determine which synchronization mechanisms should be used.Comment: To be published in IEEE CCTA2022 Proceeding

Nonlinear Observer Design for Aided Inertial Navigation of Ships

This thesis focuses on strapdowninertial navigation systems for marine vessels, exploiting low-cost micro-electro-mechanical-systems (MEMS) inertial sensors and nonlinear observers for sensor fusion. The motivation behind the research is to investigate the possibility to develop cost-effective inertial navigation systems providing, roll, pitch and heave estimates, similar to typical maritime vertical reference units (VRUs), while providing estimates of the vessel’s position, velocity and attitude as well. In addition, such systems should be fault tolerant. Nonlinear observers serve as alternatives to the well established extended Kalman filter where explicit stability properties are more cumbersome, and in some cases impossible, to achieve. The presented observers are proven to have semiglobal exponentially stability properties, where semiglobality is mainly due to the infeasibility of pure global results when considering attitude estimation of the special orthogonal group of order three. The observers are benchmarked in full-scale experiments using an established navigation suite based on the extended Kalman filter. Similar performance was obtained in state-state conditions. The observer designs are based on a framework of a nonlinear attitude observer and a translational motion observer, which forms a feedback interconnection. This is extended to incorporate a virtual vertical reference (VVR) measurement for vertical aiding of the inertial navigation system. The reference signal is utilized as an alternative to vertical measurements from position references such as global navigation satellite systems (GNSS). The inclusion of the virtual reference facilitates high-performance heave estimation. The VVR is also beneficial w.r.t. to attitude estimation improving the roll and pitch estimates by exploiting kinematics couplings between the orientational and the translational motion. In addition to including the VVR in the inertial navigation system, the observer structures are further extended to employ time-varying gains. Moreover, the VVR concept is improved utilizing an error model based on sea-state-dependent parameters. Simulations indicate that the industry standard VRU performance specification of five centimeters or five per cent root-mean-square heave error is obtainable with the proposed design. The presented observer structure are also validated using sensor data gathered on an offshore vessel in operation. Access to the mean motion of the vessel prevents unwanted motion compensation by the control system. Therefore, marine surface craft often apply wave filtering of position and heading measurements in order to reconstruct the mean motion of the vessel, by attenuating the oscillatory motion components, due to waves, embedded in the measurements. In this work, wave filtering based, on estimated and measured signals from the inertial navigation system and inertial sensors, is presented. The presented work is the first to do so, serving as an alternative to traditional observer-based approaches exploiting ship models. The thesis also consider fault tolerance and sensors redundancy using nonlinear observers. Concepts for fault detection and isolation using position, heading and inertial measurement are presented based on triple-redundant sensor configurations and nonlinear observers. Outcomes related to weighting and averaging of multiple sensor systems are also presented. In contrast to the VRU systems on board offshore vessels, where the measurements often are average on the output, this theses also focuses on weighting directly on the underlying inertial measurements before these are utilized as to estimate the VRU solution. In addition, position, velocity and heading estimates are also obtained with the approach proposed. Hence, the thesis provides a more integrated design compared to the current industrial practice, that is relying on more separated sensors. The fault tolerance properties of the concepts for redundant inertial measurement units (IMUs), are validated using inertial sensors data gather at sea, injected with artificial faults. In addition, the dissertation presents a study on the difference between loosely and tightly coupled integration in the context of nonlinear observers and on how these can be implemented in discrete time. Using data gather during an unmanned aerial vehicle flight, results related to the performance difference between these two integration techniques, based on measurements from a MEMS IMU and a standalone GNSS receiver, relative to a real time kinematic GNSS positioning solution, are presented. Finally, the algorithms presented in this thesis have the potential to be implemented and used commercially on ships and on other types of marine surface vessels. The concepts presented have the potential to increase fault tolerance. Multiple MEMS IMUs have to be installed in order to achieve this, however, these combined with the presented methods have the potential to replace existing VRU solutions. Therefore, the cost might be reduced, while the navigation system’s performance and its fault-tolerance properties may be increased

Fault-tolerant Sensor Fusion Based on Inertial Measurements and GNSS

The standard observer for inertial navigation system (INS) have for many years been the extended Kalman filter. Due to extensive research, in recent years, on nonlinear observer applied with low-cost inertial sensors can this possible change.Fault-tolerance are in many applications necessary. In dynamic positioning operations are fault-tolerance required. This thesis dealt with development of a fault-tolerant nonlinear observer for integration of INS and Global Navigation Satellite Systems (GNSS). Furthermore, the observer was applied for dynamic positioning, by developing a simulator to obtain vessel motion and sensor readings. The main focus were on GNSS errors and faults. Based on this were methods used to detect and handle outlier detection, sensor freeze, high variance of GNSS sensors and GNSS bias. Furthermore, a novel GNSS drift detection algorithm, applicable for marine vessel, was developed. Moreover, senor voting and sensor weighting was carried out by developing a voting algorithm. Also a model-based observer was utilized to provide redundant acceleration information to the INS.The chosen INS/GNSS observer proved to be a good basis for the fault-tolerant additions. Outliers, sensor freeze, high variance and bias of the GNSS sensors were detected and handled accordingly. GNSS drift was detected and a possible drive-off situation was prevented. Furthermore, utilizing a model-based observer to obtain redundant acceleration information was shown to be successful

Design of inertial navigation systems for marine craft with adaptive wave filtering aided by triple-redundant sensor packages

Marine craft feedback control systems typically require estimates of position, velocity and heading where the wave-induced motions should be suppressed. This paper presents a strapdown inertial navigation system with adaptive wave filtering. Wave filtering based on inertial navigation systems differ from previous vessel-model-based designs that require knowledge of vessel parameters and mathematical models for estimation of thruster and wind forces and moments based on auxiliary sensors. The origin of the inertial navigation system's error states is proven to be uniformly semiglobally exponentially stable. The wave-filtering scheme uses the estimated states of the inertial navigation system to separate the low-frequency motion of the craft from the wave-frequency motions. The observer structure also allows for estimation of the time-varying encounter frequency by using a signal-based frequency tracker or an adaptive observer. Finally, properties following from the triple-redundant sensor packages have been utilized to obtain optimal and robust sensor fusion with respect to sensor performance and faults. Copyright © 2015 John Wiley & Sons, Ltd

Phased array radio system aided inertial navigation for unmanned aerial vehicles

Two of the major challenges with beyond visual line of sight (BVLOS) operations for unmanned aerial vehicles (UAVs) today are navigation and communication. This paper presents a solution that takes on both problems simultaneously, using a phased array radio system (PARS) both for communication and to aid a micro-electro-mechanical inertial navigation system (INS), estimating position, velocity and attitude. The solution is independent of global navigation satellite system (GNSS) for positioning and highly resistant to malicious sources, such as spoofing and jamming. The state estimator presented in this paper fuses range and bearing measurements from the PARS with the measurements from an on-board inertial measurement unit, a magnetometer and a barometer. By aiding the INS with PARS position measurements, magnetometer readings and barometric measurements, drift-free PVA estimates are obtained. The PARS measurements can be used for navigation alongside today's GNSS solutions, or as a redundant backup system running in parallel. To validate the observer, an experiment was carried out with a fixed wing UAV on an approximately 35 minute flight with a maximal distance of 5.35 km from the base station. During this flight a root-mean-square accuracy of 26.3 m compared to a real-time kinematic GNSS solution was achieved

Robust and secure UAV navigation using GNSS, phased-array radio system and inertial sensor fusion

Positioning using global navigation satellite systems (GNSS) has for several years been the de facto method for long-range navigation of ground, marine and aerial vehicles. With global coverage, high accuracy, and lightweight receivers, GNSS positioning has several desirable properties, especially on unmanned aerial systems (UAVs) with limited sensor payload capacity. However, due to the low signal-to-noise ratio (SNR) of the GNSS signals the navigation signal is prone to malicious attacks, such as jamming or spoofing. In the last few years, alternative solutions for absolute positioning of unmanned vehicles have emerged. One example of this is positioning using a phased array radio systems (PARS). PARS equipment has the potential to provide position measurements that are accurate within tens of meters. The PARS solutions typically have significantly higher SNR and strongly encrypted messages, which makes them robust towards malicious attacks. This paper presents a method for an inertial navigation system which is aided using redundant position sensors. The high-accuracy RTK solution is the primary position reference, when it is available. The PARS is used to detect if GNSS solution is being spoofed (or jammed), and is used as the fall-back positioning solution

Pose Estimation of UAVs Based on INS Aided by Two Independent Low-cost GNSS Receivers

Increasing use of UAVs in high-precision applications, such as georeferencing and photogrammetry, increases the requirements on the accuracy of the estimated position, velocity and attitude of the vehicle. Commercial systems that utilize magnetometers in the heading estimates are cheap, but are affected by disturbances from both the vehicle itself, nearby metal structures and variations in the Earth's magnetic field. On the other side, commercial dual-antenna satellite navigation systems can provide the required accuracy, but are expensive. This paper explores the use of a low-cost setup using two independent GNSS receivers, aiding an inertial navigation system by using pseudorange, Doppler frequency and carrier phase measurements from two longitudinally separated receivers on a fixed-wing UAV. The sensor integration was based on a multiplicative extended Kalman filter (MEKF). The main contribution of this paper is the derivation of measurement models for the raw GNSS measurements based on the MEKF error state, taking into account antenna lever arms and explicitly including the difference in measurement time between the receivers in the measurement model for double differenced carrier phase. The proposed method is verified using data collected from a UAV flight

Aided Inertial Navigation of Small Unmanned Aerial Vehicles Using an Ultra-Wideband Real Time Localization System

This paper presents an ultra-wideband (UWB) radio aided inertial navigation system (INS), estimating position, velocity and attitude (PVA), based on a low-cost microelectro-mechanical system (MEMS) Inertial Measurement Units (IMUs). This ensures that a drift free INS is available for local unmanned aerial vehicle (UAV) navigation independent of global navigation satellite systems (GNSS). The experimental results show that the presented integration of UWB and INS is promising for navigating independent of satellite-based positioning systems, and illustrates the possible enhancements that are possible when adding an additional vertical position measurement.acceptedVersion© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding

Integrity monitoring (IM) in autonomous navigation has been extensively researched, but currently available solutions are mainly applicable to specific algorithms and sensors, or limited by linearity or 'Gaussianity' assumptions. This study investigates a Solution Separation (SS) based framework for universal IM, scalable to multi-sensor fusion as each hypothesis assumes a whole sensor measurement set as faulty. Architecturally we consider that: 1) multi sensor systems must account for various sensor noise models which lead to inconsistent estimates of uncertainties, 2) a module must be able to detect sensor failure or sensor noise mismodeling and suggest better bounds for the error, without being constantly conservative, 3) some algorithms are computationally heavy to monitor in the SS setting or the provided covariances cannot be interpreted in IM. A hybrid SS architecture can be practical, where some solutions are evaluated with a navigation algorithm with known characteristics, although the all-sensor-in solution is evaluated with the monitored algorithm. Experiments are run on filter and smoothing-based navigation algorithms. In addition, we experiment with hybrid SS monitoring and time-correlated noise to evaluate the appropriability of our framework in the context of the above-mentioned requirements. This is a novel framework in the IM domain, directly integrable in existing navigation solutions and, in our opinion, it will facilitate the quantification of the effect of different sensors in navigation safety