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1. A Proposed Framework for UAS Positioning in GPS-Denied and GPS-Spoofed Environments

   https://doi.org/10.1109/ICNS60906.2024.10550601  

   Burbank, Jack L; Foust, Landon; Greene, Trevor; Kaabouch, Naima (April 2024, IEEE)

   NA (Ed.)

   Unmanned Aerial Systems have become ubiquitous and are now widely used in commercial, consumer, and military applications. Their widespread use is due to a combination of their low cost, high capability, and ability to perform tasks and go places that are not easy or safe for humans. Most UAS platforms are dependent on Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), to provide positioning information for navigation and flight control. Without reliable GPS signals, the flight path cannot be trusted, and flight safety cannot be assured. However, GPS is vulnerable to several types of malicious attacks, including jamming, spoofing, or physical attacks on the GPS constellation itself. Additionally, there are environments in which GPS reception is not always possible, a key example being urban canyon areas where line-of-site to the GPS satellite constellation may be blocked or obscured by large obstacles such as buildings. Numerous methods have been proposed for position estimation in GPS denied environments. However, these methods have significant limitations and typically exhibit poor performance in certain environments and scenarios. This paper analyzes the strengths and weaknesses of existing alternate positioning methods and describes a framework where multiple positioning solutions are jointly employed to construct an optimal position estimate. The proposed framework aims to reduce computation complexity and yield good positioning performance across a wide variety of environments. 

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2. GPS-Denied State Estimation for Blue/NDAA Unmanned Multi-Rotor Vehicles

   https://doi.org/10.2514/6.2023-2666  

   Phillips, Grant; Bradley, Justin M.; Ganesh, Prashant (January 2023, AIAA SciTech 2023 Forum)

   Unmanned Aircraft Vehicle (UAV) state estimation and navigation in GPS-denied environments has received a great deal of attention, with several researchers exploring a variety of compensating estimation methods. These methods vary in capability, and usually trade off estimation accuracy for simplicity and fewer resource requirements. More advanced estimation schemes, while capable of providing good state estimates for longer periods of time, may not be suitable for small, limited resource vehicles such as UAVs. Simpler and less-accurate estimation methods, while less capable, are useful for introducing the topic to students as well as helping researchers establish flight capabilities, and may be more suitable on limited hardware. The Autonomous Vehicle Laboratory’s (AVL) REEF Estimator was designed to expedite the development of a group’s GPS-denied flight capabilities through its simple and modular design. This work seeks to extend the application of the REEF Estimator by adapting it to fit the Ardupilot flight stack so that the estimator may be used on a readily available and NDAA-compliant flight controller, specifically, a Pixhawk Cube Blue. In addition, the REEF Estimator has been containerized to further facilitate its deployment between different vehicle architectures with minimal need for reconfiguration or setup. 

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3. A Safety Constrained Control Framework for UAVs in GPS Denied Environment

   https://doi.org/10.1109/CDC42340.2020.9304304  

   Wan, Wenbin; Kim, Hunmin; Hovakimyan, Naira; Sha, Lui; Voulgaris, Petros G. (December 2020, 59th IEEE Conference on Decision and Control)

   null (Ed.)

   Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can lead to potentially dangerous situations. To avoid intolerable sensor drifts in the presence of GPS spoofing attacks, we propose a safety constrained control framework that adapts the UAV at a path re-planning level to support resilient state estimation against GPS spoofing attacks. The attack detector is used to detect GPS spoofing attacks and provides a switching criterion between the robust control mode and emergency control mode. An attacker location tracker (ALT) is developed to track the attacker's location and estimate the spoofing device's output power by the unscented Kalman filter (UKF) with sliding window outputs. Using the estimates from ALT, we design an escape controller (ESC) based on the model predictive controller (MPC) such that the UAV escapes from the effective range of the spoofing device within the escape time. 

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4. Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges

   https://doi.org/10.1109/PLANS53410.2023.10139997  

   Saha, Swapnil Sayan; Du, Yayun; Sandha, Sandeep Singh; Garcia, Luis Antonio; Jawed, Mohammad Khalid; Srivastava, Mani (April 2023, 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS))

   Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31–134 x smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward. 

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5. Relative multiplicative extended Kalman filter for observable GPS-denied navigation

   https://doi.org/10.1177/0278364920903094  

   Koch, Daniel_P; Wheeler, David_O; Beard, Randal_W; McLain, Timothy_W; Brink, Kevin_M (June 2020, The International Journal of Robotics Research)

   This work presents a multiplicative extended Kalman filter (MEKF) for estimating the relative state of a multirotor vehicle operating in a GPS-denied environment. The filter fuses data from an inertial measurement unit and altimeter with relative-pose updates from a keyframe-based visual odometry or laser scan-matching algorithm. Because the global position and heading states of the vehicle are unobservable in the absence of global measurements such as GPS, the filter in this article estimates the state with respect to a local frame that is colocated with the odometry keyframe. As a result, the odometry update provides nearly direct measurements of the relative vehicle pose, making those states observable. Recent publications have rigorously documented the theoretical advantages of such an observable parameterization, including improved consistency, accuracy, and system robustness, and have demonstrated the effectiveness of such an approach during prolonged multirotor flight tests. This article complements this prior work by providing a complete, self-contained, tutorial derivation of the relative MEKF, which has been thoroughly motivated but only briefly described to date. This article presents several improvements and extensions to the filter while clearly defining all quaternion conventions and properties used, including several new useful properties relating to error quaternions and their Euler-angle decomposition. Finally, this article derives the filter both for traditional dynamics defined with respect to an inertial frame, and for robocentric dynamics defined with respect to the vehicle’s body frame, and provides insights into the subtle differences that arise between the two formulations. 

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