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Protection of urban critical infrastructures (CIs) from GPS-denied, bomb-carrying kamikaze drones (G-BKDs) is very challenging. Previous approaches based on drone jamming, spoofing, communication interruption and hijacking cannot be applied in the case under examination, since G-B-KDs are uncontrolled. On the other hand, drone capturing schemes and electromagnetic pulse (EMP) weapons seem to be effective. However, again, existing approaches present various limitations, while most of them do not examine the case of G-B-KDs. This paper, focuses on the aforementioned under-researched field, where the G-B-KD is confronted by two defensive drones. The first neutralizes and captures the kamikaze drone, while the second captures the bomb. Both defensive drones are equipped with a net-gun and an innovative algorithm, which, among others, estimates the locations of interception, using a real-world trajectory model. Additionally, one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk. Extensive simulated experiments and comparisons to state-of-art methods, reveal the advantages and limitations of the proposed approach. More specifically, compared to state-of-art, the proposed approach improves: (a) time to neutralize the target by at least 6.89%, (b) maximum number of missions by at least 1.27% and (c) total cost by at least 5.15%. [ABSTRACT FROM AUTHOR]

Modern systems and devices, including unmanned aerial systems (UASs), autonomous vehicles, and other unmanned and autonomous systems, commonly rely on the Global Positioning System (GPS) for positioning, navigation, and timing (PNT). Cellular mobile devices rely on GPS for PNT and location-based services. Many of these systems cannot function correctly without GPS; however, GPS signals are susceptible to a wide variety of signal-related disruptions and cyberattacks. GPS threat detection and mitigation have received significant attention recently. There are many surveys and systematic reviews in the literature related to GPS security; however, many existing reviews only briefly discuss GPS security within a larger discussion of cybersecurity. Other reviews focus on niche topics related to GPS security. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security, with an emphasis on UAS applications. This paper provides an overview of the threats to GPS and the state-of-the-art techniques for attack detection and countermeasures. Detection and mitigation approaches are categorized, and the strengths and weaknesses of existing approaches are identified. This paper also provides a comprehensive overview of the state-of-the-art on alternative positioning and navigation techniques in GPS-disrupted environments, discussing the strengths and weaknesses of existing approaches. Finally, this paper identifies gaps in existing research and future research directions. [ABSTRACT FROM AUTHOR]

Protection of urban critical infrastructures (CIs) from GPS-denied, bomb-carrying kamikaze drones (G-B-KDs) is very challenging. Previous approaches based on drone jamming, spoofing, communication interruption and hijacking cannot be applied in the case under examination, since G-B-KDs are uncontrolled. On the other hand, drone capturing schemes and electromagnetic pulse (EMP) weapons seem to be effective. However, again, existing approaches present various limitations, while most of them do not examine the case of G-B-KDs. This paper, focuses on the aforementioned under-researched field, where the G-B-KD is confronted by two defensive drones. The first neutralizes and captures the kamikaze drone, while the second captures the bomb. Both defensive drones are equipped with a net-gun and an innovative algorithm, which, among others, estimates the locations of interception, using a real-world trajectory model. Additionally, one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk. Extensive simulated experiments and comparisons to state-of-art methods, reveal the advantages and limitations of the proposed approach. More specifically, compared to state-of-art, the proposed approach improves: (a) time to neutralize the target by at least 6.89%, (b) maximum number of missions by at least 1.27% and (c) total cost by at least 5.15%.

This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms. [ABSTRACT FROM AUTHOR]

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data, especially in the event of a noisy or unreliable satellite signal. Toward this end, we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera, an altimeter, a compass, and an open-source Vector Map (VMAP). The algorithm combines the matching and particle filter methods. Shape vector and correlation between two building contour vectors are defined, and a coarse-to-fine building vector matching (CFBVM) method is proposed in the matching stage, for which the original matching results are described by the Gaussian mixture model (GMM). Subsequently, an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles, and a credibility indicator is designed to avoid location mistakes in the particle filter stage. An experimental evaluation of the approach based on flight data is provided. On a flight at a height of 0.2 km over a flight distance of 2 km, the aircraft is geo-localized in a reference map of 11,025 km2 using 0.09 km² aerial images without any prior information. The absolute localization error is less than 10 m. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have proliferated across diverse domains. However, optimal UAV operations necessitate precise and reliable navigation systems. UAVs predominantly rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS), for navigation. Nevertheless, GNSS signals are susceptible to blockage, reflection, and spoofing, introducing significant risks, including navigation loss and potential UAV loss. This research investigates cutting-edge navigation solutions, emphasizing deep learning-based visual localization approaches tailored for UAVs. Our focus is on scenarios characterized by GPS-denied environments where GPS signals may be absent or unreliable. We provide a comprehensive review of contemporary deep learning-based visual localization approaches and compare them to traditional aerial visual localization methods, such as template matching and feature matching. This comparison highlights both the potential benefits and challenges associated with these approaches. Furthermore, we systematically evaluate and classify recent deep learning-based methods based on main criteria, including model type/architecture, reference imagery, operational context, and resultant accuracy levels. Our findings underscore the substantial promise inherent in various approaches while also shedding light on their unique deployment challenges. Finally, we discuss potential research directions, to inspire further innovations and progress in this domain. The ultimate goal is to develop more accurate, dependable, and secure navigation solutions for UAVs.

Modern systems and devices, including unmanned aerial systems (UASs), autonomous vehicles, and other unmanned and autonomous systems, commonly rely on the Global Positioning System (GPS) for positioning, navigation, and timing (PNT). Cellular mobile devices rely on GPS for PNT and location-based services. Many of these systems cannot function correctly without GPS; however, GPS signals are susceptible to a wide variety of signal-related disruptions and cyberattacks. GPS threat detection and mitigation have received significant attention recently. There are many surveys and systematic reviews in the literature related to GPS security; however, many existing reviews only briefly discuss GPS security within a larger discussion of cybersecurity. Other reviews focus on niche topics related to GPS security. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security, with an emphasis on UAS applications. This paper provides an overview of the threats to GPS and the state-of-the-art techniques for attack detection and countermeasures. Detection and mitigation approaches are categorized, and the strengths and weaknesses of existing approaches are identified. This paper also provides a comprehensive overview of the state-of-the-art on alternative positioning and navigation techniques in GPS-disrupted environments, discussing the strengths and weaknesses of existing approaches. Finally, this paper identifies gaps in existing research and future research directions.

This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms.

Optimized PTP (Point to Point) Navigation by customized mobile agent constitutes extensive research domain in Machine Intelligence. Distinct Literature survey confirms the proposal of SLAM (Simultaneous Localization And Mapping) mediated sensor usage for visual data collection in a particular GPS (Global Positioning System) - denied indoor environment. This has been further followed by fused data collection using multi sensor for obtaining long time and high precision localization and obstacle detection in MRN (Mobile Robot Navigation). This paper proposes an idea to incorporate machine learning module for data modeling so that the obstacle detection and identification results in relatively more precise, accurate and efficient. The comparative numerical analysis has been presented to establish the proposed methodology. Along with this, Fusion SLAM algorithm has been created and proposed which combines features of both 2D LiDAR (Light Detection And Ranging) and RGB-D (Red, Green, Blue Depth) SLAM, resulting in visual reference of combined detected periphery and reconstructed 3D depth of on-path obstacles. The aforesaid procedure is further extended with integration of Bio Intelligent path planning approach. Experimental analysis carried out with sample candidate functions with respect to concerned mobile robot and considered indoor environment has been presented which would prove to be important citation for further research in the domain of collision-free optimum MRN. [ABSTRACT FROM AUTHOR]

The need to successfully navigate in the absence of GNSS has grown in recent years. In particular, light aircraft such as UAVs are growing in popularity for a variety of applications vulnerable to GPS denial. The research presented here develops a GPS-denied navigation scheme for light aircraft employing images formed from a synthetic aperture radar system. Past research has explored the utility of radar telemetry in GPS-denied systems. This research advances previous work by exploiting radar images to obtain range and cross-range position measurements. Images are formed using the Range-Doppler Algorithm, an efficient image formation algorithm ideal for the sometimes limited processing packages available to light aircraft. An inertial navigation and radar processing system is implemented using both real and simulated radar images to aid in estimating an aircraft’s state in a GPS-denied environment. The results show that navigation in the absence of GPS using synthetic aperture radar is feasible with converging and bounded estimation errors. [ABSTRACT FROM AUTHOR]

Magnetic anomaly navigation is gaining popularity as an alternative-navigation system that addresses the challenges of GPS denial. Magnetic anomaly navigation has recently been flight test proven on an F-16 aircraft. The corrupting magnetic environment on this operational platform is 2–3 orders of magnitude larger than previous flight-tests on geo-survey aircraft, while also displaying stochastic drift. The difficult magnetic environment of the platform necessitated a combination of batch and online calibration to obtain accurate navigation solutions. Superiority of the new method is shown in comparison to prior published methods. Real navigation results on the F-16 are shown to obtain accuracies 50% better than these competing methods, with low-altitude results obtaining 59-m distance root-mean-squared errors on 1.5 h flights versus 111 m using batch-only calibrations. [ABSTRACT FROM AUTHOR]

The benefits of GNSS have created dependencies on navigation in modern day aviation systems. Many of these systems operate with no backup navigation source. This makes the capabilities supported by precise navigation vulnerable. This paper investigates a contemporary approach to terrain‐referenced navigation (TRN), used to preserve an aircraft's navigation solution during periods of GNSS denial. Traditionally, TRN has been accomplished using a single measurement sensor pointed nadir to an aircraft. Although shown to be effective, this approach limits the achievable navigation accuracy by restricting the measurable terrain gradients to those below an aircraft. This paper explores an alternative approach to TRN that maximizes navigation information through optimal steering of a gimbaled laser. A Cramér‐Rao lower bound analysis as well as a high‐fidelity simulation establishes the utility of optimal steering while employing TRN. This original approach to TRN shows the order of magnitude improvements in navigation performance over existing methods. [ABSTRACT FROM AUTHOR]

Robustness of aerial-ground multi-source image matching is closely related to the quality of the ground reference image. To explore the influence of reference images on the performance of air-ground multi-source image matching, we focused on the impact of the control point projection accuracy and tie point accuracy on bundle adjustment results for generating digital orthophoto images by using the Structure from Motion algorithm and Monte Carlo analysis. Additionally, we developed a method to learn local deep features in natural environments based on fine-tuning the pre-trained ResNet50 model and used the method to match multi-scale, multi-seasonal, and multi-viewpoint air-ground multi-source images. The results show that the proposed method could yield a relatively even distribution of feature corresponding points under different conditions, seasons, viewpoints, illuminations. Compared with state-of-the-art hand-crafted computer vision and deep learning matching methods, the proposed method demonstrated more efficient and robust matching performance that could be applied to a variety of unmanned aerial vehicle self- and target-positioning applications in GPS-denied areas.

In this brief, we study the distance-based navigation problem of unmanned aerial vehicles (UAVs) by using a single landmark placed at an arbitrarily unknown position. To solve the problem, we propose an integrated estimation-control scheme to simultaneously accomplishes two objectives: relative localization using only distance and odometry measurements, and navigation to the desired location under bounded control input. Asymptotic convergence is obtained by invoking the discrete-time LaSalle’s invariance principle in the noise-free case, and the stability under distance measurement noise is also investigated. We also validate our theoretical findings on quadcopters equipped with ultra-wideband ranging sensors and optical flow sensors in a global positioning system (GPS)-less environment. [ABSTRACT FROM AUTHOR]

A GPS-denied unmanned aerial vehicle (UAV) (Agent B) is localized through inertial navigation system alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals' direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored. [ABSTRACT FROM AUTHOR]

Unlike many current navigation approaches for micro air vehicles, the relative navigation (RN) framework presented in this paper ensures that the filter state remains observable in GPS-denied environments by working with respect to a local reference frame. By subtly restructuring the problem, RN ensures that the filter uncertainty remains bounded, consistent, and normally-distributed, and insulates flight-critical estimation and control processes from large global updates. This paper thoroughly outlines the RN framework and demonstrates its practicality with several long flight tests in unknown GPS-denied and GPS-degraded environments. The relative front end is shown to produce low-drift estimates and smooth, stable control while leveraging off-the-shelf algorithms. The system runs in real time with onboard processing, fuses a variety of vision sensors, and works indoors and outdoors without requiring special tuning for particular sensors or environments. RN is shown to produce globally-consistent, metric, and localized maps by incorporating loop closures and intermittent GPS measurements. [ABSTRACT FROM AUTHOR]

A typical navigation system for a Micro Aerial Vehicle (MAV) relies basically on GPS for position estimation. However, for several kinds of applications, the precision of the GPS is inappropriate or even its signal can be unavailable. In this context, and due to its flexibility, Monocular Simultaneous Localization and Mapping (SLAM) methods have become a good alternative for implementing visual-based navigation systems for MAVs that must operate in GPS-denied environments. On the other hand, one of the most important challenges that arises with the use of the monocular vision is the difficulty to recover the metric scale of the world. In this work, a monocular SLAM system for MAVs is presented. In order to overcome the problem of the metric scale, a novel technique for inferring the approximate depth of visual features from an ultrasonic range-finder is developed. Additionally, the altitude of the vehicle is updated using the pressure measurements of a barometer. The proposed approach is supported by the theoretical results obtained from a nonlinear observability test. Experiments performed with both computer simulations and real data are presented in order to validate the performance of the proposal. The results confirm the theoretical findings and show that the method is able to work with low-cost sensors. [ABSTRACT FROM AUTHOR]

Combined vision system is a perspective display concept to enhance a situation awareness of the pilots during aircraft landing, which integrates a real 2-D image captured from forward-looking infrared camera with a synthetic 3-D image derived by the aircraft pose and terrain database. However, the inertial measured errors significantly affect the conformal display of combined vision. This article proposes a novel method for real and synthetic images registration based on visual–inertial fusion. It includes the following key steps: (1) detect and extract the real runway features from forward-looking infrared image; (2) generate the synthetic runway features simultaneously; (3) set up vision measurement model with real and synthetic runway features; (4) integrate inertial data and visual observations in the square-root unscented Kalman filter; (5) create a synthetic 3-D scene by the filtered pose data and integrate it with a real 2-D image. The experimental results demonstrate that our method can guarantee the conformal display of combined vision system in GPS-denied and low visibility conditions. [ABSTRACT FROM AUTHOR]

In this work, a novel monocular simultaneous localization and mapping (SLAM) system with application to micro aerial vehicles is proposed. The main difference with respect to previous approaches is that a barometer is used as a unique sensory aid for incorporating altitude information into the system in order to recover an absolute metric scale. First, an observability analysis of a simplified model of a monocular SLAM system is developed. From this analysis, several theoretical results are derived. Among others, one important result is related to the fact that the metric scale can become observable when measurements of altitude are included in the system. In this case, sufficient conditions for observability are presented. The design of the proposed method is based on these theoretical results. Simulations and experiments with real data are presented to validate the proposed approach. The results confirm that the metric scale can be retrieved by including altitude measurements in the system. It is also shown that the proposed method can be practically implemented, using low-cost sensors, to perform visual-based navigation in GPS-denied environments. [ABSTRACT FROM AUTHOR]

This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a multirotor able to inspect an industrial combustion chamber thus working in a GPS-denied environment with poor lighting conditions, in the presence of magnetic and communication disturbances, iron dust and repetitive patterns on the structure walls. The presented system is able to pass through narrow entrances but still capable of acquiring high resolution images and to allow operators to perform inspection of the structures. Starting from the captured data, the system is able to provide a 3D reconstruction of the inspected environment for offline analysis. [ABSTRACT FROM AUTHOR]

In this article, we present a monocular visual reactive navigation system capable of navigating at high speeds, without GPS, in unknown complex cluttered environments. The system, called R-ADVANCE (Rapid Adaptive Prediction for Vision-based Autonomous Navigation, Control, and Evasion), consists of a set of biologically inspired visual perception and reactive control algorithms that provide low-computation reactive obstacle avoidance while navigating at high speeds in search of a goal object. These algorithms, along with basic planning, and augmented with low-precision visual odometry, were implemented on a micro unmanned aerial vehicle and tested in a number of challenging environments. While each of the individual algorithmic and hardware elements has been previously studied in limited environments, this work is the first time that these novel components have been integrated and flight-tested. To achieve fast flight, an NVIDIA Tegra TK1 was used as the main processor, allowing us to parallelize the system to process 1280 × 720 video streams at 40 fps, reaching flight speeds up to 19 m/s (≈68 km/h) or 42 mph. [ABSTRACT FROM AUTHOR]

Using a camera, a micro aerial vehicle (MAV) can perform visual-based navigation in periods or circumstances when GPS is not available, or when it is partially available. In this context, the monocular simultaneous localization and mapping (SLAM) methods represent an excellent alternative, due to several limitations regarding to the design of the platform, mobility and payload capacity that impose considerable restrictions on the available computational and sensing resources of the MAV. However, the use of monocular vision introduces some technical difficulties as the impossibility of directly recovering the metric scale of the world. In this work, a novel monocular SLAM system with application to MAVs is proposed. The sensory input is taken from a monocular downward facing camera, an ultrasonic range finder and a barometer. The proposed method is based on the theoretical findings obtained from an observability analysis. Experimental results with real data confirm those theoretical findings and show that the proposed method is capable of providing good results with low-cost hardware. [ABSTRACT FROM AUTHOR]

This paper proposes a novel infrared-inertial navigation method for the precise landing of commercial aircraft in low visibility and Global Position System (GPS)-denied environments. Within a Square-root Unscented Kalman Filter (SR_UKF), inertial measurement unit (IMU) data, forward-looking infrared (FLIR) images and airport geo-information are integrated to estimate the position, velocity and attitude of the aircraft during landing. Homography between the synthetic image and the real image which implicates the camera pose deviations is created as vision measurement. To accurately extract real runway features, the current results of runway detection are used as the prior knowledge for the next frame detection. To avoid possible homography decomposition solutions, it is directly converted to a vector and fed to the SR_UKF. Moreover, the proposed navigation system is proven to be observable by nonlinear observability analysis. Last but not least, a general aircraft was elaborately equipped with vision and inertial sensors to collect flight data for algorithm verification. The experimental results have demonstrated that the proposed method could be used for the precise landing of commercial aircraft in low visibility and GPS-denied environments.

Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtained in previous flights, with synchronized images, GPS and IMU data. The estimated position in each landmark recognition was compatible with the GPS data, stating that the developed method can be used as an alternative navigation system. [ABSTRACT FROM AUTHOR]

The primary method for geo-localization is based on GPS which has issues of localization accuracy, power consumption, and unavailability. This paper proposes a novel approach to geo-localization in a GPS-denied environment for a mobile platform. Our approach has two principal components: public domain transport network data available in GIS databases or OpenStreetMap; and a trajectory of a mobile platform. This trajectory is estimated using visual odometry and 3D view geometry. The transport map information is abstracted as a graph data structure, where various types of roads are modelled as graph edges and typically intersections are modelled as graph nodes. A search for the trajectory in real time in the graph yields the geo-location of the mobile platform. Our approach uses a simple visual sensor and it has a low memory and computational footprint. In this paper, we demonstrate our method for trajectory estimation and provide examples of geolocalization using public-domain map data. With the rapid proliferation of visual sensors as part of automated driving technology and continuous growth in public domain map data, our approach has the potential to completely augment, or even supplant, GPS based navigation since it functions in all environments. [ABSTRACT FROM AUTHOR]

Robustness of aerial-ground multi-source image matching is closely related to the quality of the ground reference image. To explore the influence of reference images on the performance of air-ground multi-source image matching, we focused on the impact of the control point projection accuracy and tie point accuracy on bundle adjustment results for generating digital orthophoto images by using the Structure from Motion algorithm and Monte Carlo analysis. Additionally, we developed a method to learn local deep features in natural environments based on fine-tuning the pre-trained ResNet50 model and used the method to match multi-scale, multi-seasonal, and multi-viewpoint air-ground multi-source images. The results show that the proposed method could yield a relatively even distribution of feature corresponding points under different conditions, seasons, viewpoints, illuminations. Compared with state-of-the-art hand-crafted computer vision and deep learning matching methods, the proposed method demonstrated more efficient and robust matching performance that could be applied to a variety of unmanned aerial vehicle self- and target-positioning applications in GPS-denied areas. [ABSTRACT FROM AUTHOR]

This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to reduce the attitude error and position drift (both horizontal and vertical) with the dual objective of improving the aircraft GNSS-Denied inertial navigation capabilities as well as facilitating the fusion of the inertial filter with visual odometry algorithms. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results, compare the proposed filter with more traditional implementations, and analyze the sensitivity of the results to the quality of the onboard sensors. The author releases the ▪ implementation of both the navigation filter and the high fidelity simulation as open-source software [1]. [ABSTRACT FROM AUTHOR]