Low-Signal Navigation for Drones

Structured light navigation aid for autonomous vehicles that uses emitted structured light patterns to improve navigation in GNSS-denied environments. The aid has a movable structured light emitter that projects patterns onto surfaces. A receiver captures reflected light to calculate navigation info. This provides a fixed, drift-free source vs. GNSS-prone drift. The emitter can change direction and pattern. This allows scanning to find suitable landing spots based on features.

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Unmanned aerial vehicle (UAV) localization system that enables precise determination of its absolute position in complex environments through machine learning-based 3D point cloud registration. The system employs a trained model that generates both semantic and depth representations of the environment from camera images, which are then registered against a pre-existing point cloud. This registration process enables accurate determination of the UAV's position and orientation, even in environments with GPS signal loss or sensor malfunctions. The system can be used for autonomous missions where GPS is unavailable or unreliable.

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Optimizing UAV flight paths through AI-driven radio condition adaptation. The method enables UAVs to automatically adjust their flight paths in response to changing radio conditions, leveraging machine learning models to determine optimal path modifications. The approach integrates with UAV networks to dynamically configure network nodes and UAVs based on real-time radio environment conditions, ensuring seamless path adaptation while maintaining network stability.

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Systems and methods for displaying connectivity strength on connected vehicles like drones to help them avoid areas with poor coverage and communication issues. The vehicles receive connectivity data from other vehicles and ground stations, identifying areas with low connectivity. This data is displayed on the vehicle to warn of upcoming poor coverage. The vehicle can also generate updated routes to avoid low connectivity areas. The ground stations transmit specific connectivity data based on vehicle location.

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Abstract: - Conventional tracking systems relying on GPS and cellular infrastructure are ineffective in environments with little or no connectivitysuch as remote wilderness, mountainous regions, disaster-affected areas. This paper introduces a hybrid architecture that integrates satellite communication, mesh networking, Radio Tomographic Imaging (RTI), signal jumping, drone-assisted relays, Low-Power Wide-Area Networks (LPWAN). The proposed system addresses loss challenges by utilizing AI-driven prediction autonomous drones to extend coverage improve real-time traceability. Performance is evaluated through simulations real-world case studies based key metrics including coverage, latency, energy efficiency, reliability. approach demonstrates strong potential for critical applications search rescue, defense operations, systems, contributing toward the development of resilient off-grid communication technologies. Keywords: Remote Tracking, Dead Zones, Mesh Networks, Satellite Communication, Drone Relays, RTI, LPWAN, Signal Prediction, Off-Grid Search Rescue.

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Enabling unmanned aerial vehicles (UAVs) to navigate autonomously without relying on cameras and compasses when environmental conditions like low light or magnetic interference impair their effectiveness. The UAV uses a global navigation satellite system (GNSS) to determine its position and velocity in a world frame of reference. It also measures acceleration and angular rate signals from onboard accelerometers and gyroscopes to determine acceleration and orientation in a navigation frame of reference. By combining GNSS positioning with inertial measurements, the UAV can navigate autonomously even when cameras and compasses are unreliable.

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Navigation system for unmanned aerial vehicles (UAVs) that can autonomously navigate and locate themselves without GPS in GPS-denied areas. The system uses onboard cameras to capture images of the terrain below the UAV. Object detection using machine learning identifies objects in the images. Comparing these detected objects with objects in a preloaded map allows locating the UAV based on matched objects. With the UAV location, it can navigate to targets using the map. This enables UAVs to continue autonomous flight in GPS-denied areas.

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The Flying Ad-Hoc Network (FANET) is an important component of the 6G communication system. In order to achieve precise positioning unmanned aerial vehicle (UAV) nodes in a FANET when satellite navigation signals are unavailable, simple and accurate direction-of-arrival (DOA) estimation methods required. this paper, we propose improved correlative interferometer method estimate DOAs UAVs FANET. This adopts uniform funnel array (UFA) configuration, which consists circular (UCA) additional element located above center. configuration improves accuracy for with large polar angles because it utilizes degree freedom vertical aperture. addition, Fibonacci sampling strategy employed overcome clustering phenomenon exhibited by latitudelongitude sampling. Furthermore, interferometer, only partial phase differences used reduce storage burden. When calculating similarity function, adopt triangular function instead cosine improve computational efficiency. simulation results show that proposed UFA DOA 65.56% over planar UCA angles. Moreover, 11.54% as compared

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System for mitigating GNSS interference and enabling continuous and accurate GNSS navigation even when GNSS signals are jammed or spoofed. The system uses RF nulling circuits at each antenna to cancel out interference signals. Virtual antenna positions are calculated based on the interference-cancelled RF signals and the physical antenna locations. These virtual positions are used by the navigation system instead of the actual antenna positions. This allows accurate navigation even with GNSS interference since the interference is removed before calculating the virtual positions.

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A method to improve the accuracy of Global Navigation Satellite System (GNSS) receivers by allowing longer coherent integration times, which increases sensitivity, even when there is dynamic motion of the receiver. The method involves using Doppler estimates derived from inertial measurements to compensate for the motion during the coherent integration interval. This allows longer coherent integration times without losing signal lock or accuracy due to dynamic motion.

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Polarized antenna for autonomous perception and navigation that can provide reliable performance in challenging conditions like poor visibility or inclement weather. The antenna uses a parallel-fed polarized structure with a compact feeding network on a single layer instead of multiple layers. It also has an end-fed serial feeding structure with wider beamwidth and reduced beam squint compared to traditional end-fed antennas. The parallel feeding reduces complexity and size compared to separate layer feeding. The end-fed serial feeding improves beam characteristics for radar applications.

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Method to improve GNSS receiver sensitivity by extending coherent integration time while mitigating the negative effects of dynamic motion. It compensates for receiver acceleration during long coherent integration by using Doppler estimates from inertial measurements instead of relying solely on the PLL/DLL. This allows increasing integration time even if the signal is too weak to lock. At each epoch, the carrier phase is initialized based on a previous Doppler estimate, then updated using current Doppler estimates. This accounts for receiver motion during integration.

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Assisted wireless device positioning using a vehicle in a wireless network to improve accuracy compared to relying solely on cell towers. The vehicle can be an unmanned aerial, ground, or responder vehicle. It associates with the target device's network, triggers positioning using signals between the vehicle, device, and fixed access points, and determines the device's position. This leverages the vehicle's proximity and potentially better radio conditions compared to the target device. It allows more accurate positioning when the target is indoors or has poor signal.

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Determining the flight path of an air vehicle like a drone using the location of a nearby device as a reference instead of absolute coordinates. The method involves acquiring the position of a ground device like a phone or sensor at a known location using corrections based on nearby reference stations. The air vehicle's flight path is then determined based on the ground device's location rather than absolute coordinates, providing more accurate and reliable path planning.

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Smart sensor array for accurate classification and localization of radio frequency sources using selective machine learning to optimize performance. The sensor array combines traditional methods like time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength indicator (RSSI) with machine learning for non-line-of-sight conditions. The machine learning is limited to specific areas predefined during calibration. By leveraging contextual information and behavior analysis, the system can differentiate between ground-based and flying emitters. This enables selective use of machine learning to reduce computational cost and latency compared to full area coverage.

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A system and method for navigating fixed-wing unmanned aerial systems (UAS) in environments without or with degraded global positioning System (GPS) signals. The method uses relative motion estimation and optimization to improve local navigation and leverage occasional GPS measurements and cooperative constraints from other UAS for global positioning. The UAS estimates its motion relative to the environment using an onboard sensor fusion algorithm like an extended Kalman filter. It then optimizes a back-end pose graph representing global position by incorporating local motion estimates and occasional GPS measurements as constraints. Sharing range measurements and resetting simultaneously between UAS allows leveraging cooperative constraints. This improves accuracy compared to relying solely on local sensing in GPS-denied environments.

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A moving body like a drone that can switch between air, water, and land environments and accurately estimate distances between itself and other communication devices in each medium. The drone estimates distances by selecting an appropriate profile based on the current medium and the transmission path characteristics between the drone and the other device. This compensates for the different signal attenuation and propagation characteristics in air, water, and land.

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Embedded position, navigation, and timing (PNT) system for satellite terminals that allows them to operate in multiple Global Navigation Satellite System (GNSS) degraded or denied environments without external input. The system receives signals from multiple constellations, evaluates them, and provides a single output to the terminal's position and timing systems. This enables seamless switching between constellations and avoids downtime. The embedded PNT system can mitigate degraded, denied, or spoofed GNSS situations for extended periods without external assistance.

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Aircraft guidance in areas with weak or compromised GPS signals using a network of mobile platforms. The aircraft calculates its position relative to a nearby mobile platform based on signals between them. It then uses the relative position and the mobile platform's known position to calculate the aircraft's absolute position. This allows accurate navigation in contested areas without relying solely on GPS.

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Generating auxiliary satellite position data to improve orbit accuracy when GPS signals are unavailable. The method involves using ground-based orbit determination to estimate satellite positions from tracking data. Future positions are predicted and sent to the satellite as auxiliary data. The satellite uses this instead of GPS to accurately determine its position. This filters out GPS noise and allows reliable positioning during GPS outages.

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