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Global navigation satellite systems (GNSS) are vital technologies of our age and serve a plethora of industries that rely on precise positioning for automation, efficiency, and safety. Emerging applications of unmanned aerial vehicles (UAV) in critical applications like security, surveillance, critical logistics and defense demand precise and robust navigation capabilities even in challenging environments with high multipath or (un-)intended interference. The design of robust GNSS receivers for UAV applications, capable of suppressing interfering signals, is challenging due to the need for multi-antenna systems and the stringent requirements on hardware to be lightweight and miniaturized enough to fit onto small mobile platforms. In order to overcome these limitations, the present article details a four-element wideband antenna array, fitting into a 100 mm diameter footprint. The array is capable to operate across all GNSS frequency bands while incorporating, if needed, a multipath mitigation solution. The antenna design relies on a modular concept with 3D printed Dielectric Resonator Antennas (DRAs) and vertical choke rings. The antenna performance is evaluated in terms of its radiation pattern via installed antenna simulations and measurements in an anechoic chamber. The effect of different installation heights on the antenna pattern is studied. Furthermore, GNSS measurements carried out with the array alone and mounted on the UAV are presented. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) location engines on Android devices provide location and navigation utility to billions of people worldwide. However, these location engines currently have very limited protection from threats to their position, navigation, and time (PNT) solutions. External sources of radio frequency interference (RFI) can render PNT information unusable. Even worse, false signals or spoofing can provide a false PNT solution to Android devices. To mitigate this, four detection methods were developed and evaluated using native location parameters within Android: Comparing the GNSS and network locations, checking the Android mock location flag, comparing the GNSS and Android system times, and observing the automatic gain control (AGC) and carrier-to-noise density (C/N0) signal metrics. These methods provide a powerful means to significantly increase the robustness of the Android GNSS-based PNT solution and are implemented in the GNSSAlarm Android application to demonstrate real-time jamming and spoofing detection. [ABSTRACT FROM AUTHOR]

Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth’s geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people in the robot’s surroundings, magnetic-based localization is ideal for service robotics in supermarkets, hotels, etc. A common approach for magnetic-based localization is to first create a magnetic map of the environment where the robot will be deployed. For this, magnetic samples acquired a priori are used. To generate this map, the collected data is interpolated by training a Gaussian Process Regression model. Gaussian processes are nonparametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. These models are flexible and generate mean predictions as well as the confidence of those predictions, making them ideal for their use in probabilistic approaches. However, their computational and memory cost scales poorly when large datasets are used for training, making their use in large-scale environments challenging. The purpose of this study is to: (i) enable magnetic-based localization on large-scale environments by using a sparse representation of Gaussian processes, (ii) test the effect of several kernel functions on robot localization, and (iii) evaluate the accuracy of the approach experimentally on different large-scale environments.

A navigation algorithm for source seeking in a sensor network environment is presented in this paper. The solution consists of a gradient-free approach and maximum likelihood topology maps of sensor networks. The robot is navigated using an angular velocity limited by maximum and minimum constants, and by sensor measurements gathered by sensors that are close to robot's current location. The location of the robot is calculated using sensor topology coordinates, which is an alternative to the physical coordinate system and does not depend on physical distance measurement techniques such as received signal strength. However, actual physical distances are hidden in topology maps due to nonlinear distortions compared to physical distance between nodes. Thus, the proposed control law does not depend on any distance-based information. The performance of the algorithm is evaluated using computer simulations and experiments with a real mobile robot. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) is the technology of choice for outdoor positioning purposes but has many limitations when used in safety-critical applications such Intelligent Transportation Systems (ITS) and Unmanned Autonomous Systems (UAS). Namely, its performance clearly degrades in harsh propagation conditions and is not reliable due to possible attacks or interference. Moreover, GNSS signals may not be available in the so-called GNSS-denied environments, such as deep urban canyons or indoors, and standard GNSS architectures do not provide the precision needed in ITS. Among the different alternatives, cellular signals (LTE/5G) may provide coverage in constrained urban environments and Ultra-Wideband (UWB) ranging is a promising solution to achieve high positioning accuracy. The key points impacting any time-of-arrival (TOA)-based navigation system are (i) the transmitters’ geometry, (ii) a perfectly known transmitters’ position, and (iii) the environment. In this contribution, we analyze the performance loss of alternative TOA-based navigation systems in real-life applications where we may have both transmitters’ position mismatch, harsh propagation environments, and GNSS-denied conditions. In addition, we propose new robust filtering methods able to cope with both effects up to a certain extent. Illustrative results in realistic scenarios are provided to support the discussion and show the performance improvement brought by the new methodologies with respect to the state-of-the-art.

There exist numerous navigation solutions already implemented into various navigation systems. Depending on the vehicle in which the navigation system is used, it can be distinguished in most cases among; navigation, tactical, and commercial grade categories of such systems. The core of these systems is formed by inertial sensors, i.e. accelerometers and angular rate sensors/gyros. Navigation and tactical grade systems commonly rely on fiber optic/ring laser gyros and servo/quartz accelerometers with high resolution, sensitivity, and stability. In the case of cost-effective navigation systems, for example piloted light and ultralight aircraft, usually use commercial grade sensors, where the situation differs. The sensor outputs are less stable and sensitive, and suffer from manufacturing limits leading to temperature dependency, bias instability, and misalignment which introduces non-negligible disturbances. These conditions commonly limit the applicability of the navigation solution since its stand-alone operation using free integration of accelerations and angular rates is not stable. This paper addresses a cost-effective solution with commercial grade inertial sensors, and studies the performance of different approaches to obtain navigation solution with robustness to GNSS outages. A main goal of this paper is thus comparison of a nonlinear observer and two extended Kalman filter solutions with respect to the accuracy of estimated quantities and their sensitivity to GNSS outages. The performance analyses are carried out on real flight data and evaluated during phases of the flight when the solutions are challenged by different environmental disturbances. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments where the GPS signal is faulty or is only present in some places and when the airspace is filled with obstacles. UAV navigation then becomes challenging because the UAV uses other sensors, which in turn generate uncertainty about its localisation and motion systems, especially if the UAV is a low cost platform. Additional uncertainty affects the mission when the UAV goal location is only partially known and can only be discovered by exploring and detecting a target. This navigation problem is established in this research as a Partially-Observable Markov Decision Process (POMDP), so as to produce a policy that maps a set of motion commands to belief states and observations. The policy is calculated and updated on-line while flying with a newly-developed system for UAV Uncertainty-Based Navigation (UBNAV), to navigate in cluttered and GPS-denied environments using observations and executing motion commands instead of waypoints. Experimental results in both simulation and real flight tests show that the UAV finds a path on-line to a region where it can explore and detect a target without colliding with obstacles. UBNAV provides a new method and an enabling technology for scientists to implement and test UAV navigation missions with uncertainty where targets must be detected using on-line POMDP in real flight scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for a miniature ducted-fan aerial robot to safely perform flight missions in an unknown densely populated environment. The main novelty of the proposed framework is that the vehicle is assumed to be totally blind, namely the obstacles cannot be sensed a priori. Therefore, the presence of possible accidental contacts with the surrounding environment has to be taken into account. To maintain stability in such a complex scenario, mechanics, and control are co-designed. The proposed control strategy relies on some mechanical properties of the airframe, namely on the relative position of the contact points and of the onboard actuators, and on the design of a supervisor able to detect the presence of a contact only by observing the behavior of the flight control loop. The effectiveness of the obtained results is then demonstrated using experiments conducted on a ducted-fan prototype. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments where the GPS signal is faulty or is only present in some places and when the airspace is filled with obstacles. UAV navigation then becomes challenging because the UAV uses other sensors, which in turn generate uncertainty about its localisation and motion systems, especially if the UAV is a low cost platform. Additional uncertainty affects the mission when the UAV goal location is only partially known and can only be discovered by exploring and detecting a target. This navigation problem is established in this research as a Partially-Observable Markov Decision Process (POMDP), so as to produce a policy that maps a set of motion commands to belief states and observations. The policy is calculated and updated on-line while flying with a newly-developed system for UAV Uncertainty-Based Navigation (UBNAV), to navigate in cluttered and GPS-denied environments using observations and executing motion commands instead of waypoints. Experimental results in both simulation and real flight tests show that the UAV finds a path on-line to a region where it can explore and detect a target without colliding with obstacles. UBNAV provides a new method and an enabling technology for scientists to implement and test UAV navigation missions with uncertainty where targets must be detected using on-line POMDP in real flight scenarios.

Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth's geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people in the robot's surroundings, magnetic-based localization is ideal for service robotics in supermarkets, hotels, etc. A common approach for magnetic-based localization is to first create a magnetic map of the environment where the robot will be deployed. For this, magnetic samples acquired a priori are used. To generate this map, the collected data is interpolated by training a Gaussian Process Regression model. Gaussian processes are nonparametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. These models are flexible and generate mean predictions as well as the confidence of those predictions, making them ideal for their use in probabilistic approaches. However, their computational and memory cost scales poorly when large datasets are used for training, making their use in large-scale environments challenging. The purpose of this study is to: (i) enable magnetic-based localization on large-scale environments by using a sparse representation of Gaussian processes, (ii) test the effect of several kernel functions on robot localization, and (iii) evaluate the accuracy of the approach experimentally on different large-scale environments. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) is the technology of choice for outdoor positioning purposes but has many limitations when used in safety-critical applications such Intelligent Transportation Systems (ITS) and Unmanned Autonomous Systems (UAS). Namely, its performance clearly degrades in harsh propagation conditions and is not reliable due to possible attacks or interference. Moreover, GNSS signals may not be available in the so-called GNSS-denied environments, such as deep urban canyons or indoors, and standard GNSS architectures do not provide the precision needed in ITS. Among the different alternatives, cellular signals (LTE/5G) may provide coverage in constrained urban environments and Ultra-Wideband (UWB) ranging is a promising solution to achieve high positioning accuracy. The key points impacting any time-of-arrival (TOA)-based navigation system are (i) the transmitters' geometry, (ii) a perfectly known transmitters' position, and (iii) the environment. In this contribution, we analyze the performance loss of alternative TOA-based navigation systems in real-life applications where we may have both transmitters' position mismatch, harsh propagation environments, and GNSS-denied conditions. In addition, we propose new robust filtering methods able to cope with both effects up to a certain extent. Illustrative results in realistic scenarios are provided to support the discussion and show the performance improvement brought by the new methodologies with respect to the state-of-the-art. [ABSTRACT FROM AUTHOR]