"Just because of this shorter distance, we will put down signals that will be approximately a hundred times stronger than the GPS signal," says Tyler Reid, chief technology officer and cofounder of Xona. "That means the reach of jammers will be much smaller against our system, but we will also be able to reach deeper into indoor locations, penetrating through multiple walls." The first GPS system went live in 1993. In the decades since, it has become one of the foundational technologies that the world depends on. The precise positioning, navigation, and timing (PNT) signals beamed by its satellites underpin much more than Google Maps in your phone. They guide drill heads at offshore oil rigs, time-stamp financial transactions, and help sync power grids all over the world.

Maxar Intelligence demonstrates its Raptor software that can guide drones through remote regions where there is no GPS signal, like this Polar Circle demonstration. A key geospatial intelligence firm on Tuesday announced a new product that can operate drones even in areas where the GPS signal has been jammed - cutting through modern defenses in the age of unmanned vehicular warfare. The war between Russia and Ukraine presented a unique problem: each military had learned how to jam the other's GPS signals, meaning their drones would be flying blind. This prompted the latest innovation from Maxar Intelligence, a drone-guiding technology that does not rely on satellite signals from space. Now, Maxar, a global satellite imagery and geospatial intelligence provider, has the capability to counter GPS-jamming technology through its Raptor system.

Autonomous Vehicles (AVs) heavily rely on sensors and communication networks like Global Positioning System (GPS) to navigate autonomously. Prior research has indicated that networks like GPS are vulnerable to cyber-attacks such as spoofing and jamming, thus posing serious risks like navigation errors and system failures. These threats are expected to intensify with the widespread deployment of AVs, making it crucial to detect and mitigate such attacks. This paper proposes GPS Intrusion Detection System, or GPS-IDS, an Anomaly Behavior Analysis (ABA)-based intrusion detection framework to detect GPS spoofing attacks on AVs. The framework uses a novel physics-based vehicle behavior model where a GPS navigation model is integrated into the conventional dynamic bicycle model for accurate AV behavior representation. Temporal features derived from this behavior model are analyzed using machine learning to detect normal and abnormal navigation behavior. The performance of the GPS-IDS framework is evaluated on the AV-GPS-Dataset - a real-world dataset collected by the team using an AV testbed. The dataset has been publicly released for the global research community. To the best of our knowledge, this dataset is the first of its kind and will serve as a useful resource to address such security challenges.

The accuracy and robustness of vehicle localization are critical for achieving safe and reliable high-level autonomy. Recent results show that GPS is vulnerable to spoofing attacks, which is one major threat to autonomous driving. In this paper, a novel anomaly detection and mitigation method against GPS attacks that utilizes onboard camera and high-precision maps is proposed to ensure accurate vehicle localization. First, lateral direction localization in driving lanes is calculated by camera-based lane detection and map matching respectively. Then, a real-time detector for GPS spoofing attack is developed to evaluate the localization data. When the attack is detected, a multi-source fusion-based localization method using Unscented Kalman filter is derived to mitigate GPS attack and improve the localization accuracy. The proposed method is validated in various scenarios in Carla simulator and open-source public dataset to demonstrate its effectiveness in timely GPS attack detection and data recovery.

Every day, billions of people use the GPS satellite system to find their way around the world--but GPS signals are vulnerable. Jamming and spoofing attacks can cripple GPS connections entirely or make something appear in the wrong location, causing disruption and safety issues. New data analysis reveals that multiple major Russian cities appear to have faced widespread GPS disruption during the past week. The signal interference follows Ukraine launching long-range drone attacks deep into Russian territory, and it may act as a way to potentially stop drones that rely upon GPS for navigation, experts say. The GPS interference has "expanded on a scale that hasn't been seen before," says Erik Kannike, a program manager at Estonian defense intelligence firm SensusQ who has been monitoring the situation.

Robotic applications are continuously striving towards higher levels of autonomy. To achieve that goal, a highly robust and accurate state estimation is indispensable. Combining visual and inertial sensor modalities has proven to yield accurate and locally consistent results in short-term applications. Unfortunately, visual-inertial state estimators suffer from the accumulation of drift for long-term trajectories. To eliminate this drift, global measurements can be fused into the state estimation pipeline. The most known and widely available source of global measurements is the Global Positioning System (GPS). In this paper, we propose a novel approach that fully combines stereo Visual-Inertial Simultaneous Localisation and Mapping (SLAM), including visual loop closures, with the fusion of global sensor modalities in a tightly-coupled and optimisation-based framework. Incorporating measurement uncertainties, we provide a robust criterion to solve the global reference frame initialisation problem. Furthermore, we propose a loop-closure-like optimisation scheme to compensate drift accumulated during outages in receiving GPS signals. Experimental validation on datasets and in a real-world experiment demonstrates the robustness of our approach to GPS dropouts as well as its capability to estimate highly accurate and globally consistent trajectories compared to existing state-of-the-art methods.

Robotics researchers at the University of Zurich show how onboard cameras can be used to keep damaged quadcopters in the air and flying stably – even without GPS. As anxious passengers are often reassured, commercial aircrafts can easily continue to fly even if one of the engines stops working. But for drones with four propellers – also known as quadcopters – the failure of one motor is a bigger problem. With only three rotors working, the drone loses stability and inevitably crashes unless an emergency control strategy sets in. Researchers at the University of Zurich and the Delft University of Technology have now found a solution to this problem: They show that information from onboard cameras can be used to stabilize the drone and keep it flying autonomously after one rotor suddenly gives out.

In Uber's ride-hailing business, a driver picks up a user from a curbside or other location, and then drops them off at their destination, completing a trip. Uber Eats, our food delivery service, faces a more complex trip model. When a user requests a food order in the app, the specified restaurant begins preparing the order. When that order is ready, we dispatch a delivery-partner to pick it up and bring it to the eater. Modeling the real world logistics that go into an Uber Eats trip is a complex problem.

ABSTRACT: T his paper aims at detecting an accurate position of the main entrance of the buildings. The proposed approach relies on the fact that the GPS signals drop significantly when the user enters a building. Moreover, as most of the public buildings provide Wi - Fi services, the W i - Fi received signal strength (RSS) can be utilized in order to detect the entrance of the buildings. The rationale behind this paper is that the GPS signals decrease as the user gets close to the main entrance and the Wi - Fi signal increases as the user ap proaches the main entrance. Several real experiments have been conducted in order to guarantee the feasibility of the proposed approach.

This paper considers a resilient state estimation framework for unmanned aerial vehicles (UAVs) that integrates a Kalman filter-like state estimator and an attack detector. When an attack is detected, the state estimator uses only IMU signals as the GPS signals do not contain legitimate information. This limited sensor availability induces a sensor drift problem questioning the reliability of the sensor estimates. We propose a new resilience measure, escape time, as the safe time within which the estimation errors remain in a tolerable region with high probability. This paper analyzes the stability of the proposed resilient estimation framework and quantifies a lower bound for the escape time. Moreover, simulations of the UAV model demonstrate the performance of the proposed framework and provide analytical results.