Low-Signal Navigation for Drones

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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Navigation system that provides reliable position estimates even when external signals are unreliable. The system uses parallel calculation units with different sets of input sources. One unit uses only inertial and terrain data, while the other adds external signals. They compare reliability and select the better estimate. This allows identifying problematic sources and continuing with a more trustworthy estimate when external signals fail.

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Determining the location of a wireless device using low-earth-orbit satellite signals when traditional GPS signals are weak. The technique involves correlating samples of the received satellite signal with a replica of the transmitted signal. By cross-correlating the re-sampled received signal with the transmitted signal replica, a sufficient length of the signal can be used to determine the time difference of arrival (TDOA) between the device and multiple satellites. This allows calculating the device's 3D position using TDOA like GPS. The re-sampling accounts for doppler shift and clock errors to align the signals for correlation.

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Moving body like a drone that improves alignment accuracy during autonomous movement by determining optimal directions based on network topology and transmission characteristics. The moving body wirelessly communicates with external devices to acquire network connection relationships and transmission path characteristics. It then uses this information to determine directions for movement, rather than relying solely on sensors or GPS. This improves alignment accuracy, especially in environments where visibility or accuracy is poor.

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Magnetic navigation and localization system that uses onboard magnetometers instead of GPS to provide more reliable positioning in areas where GPS is unreliable. The system leverages the stable and predictable magnetic field of the Earth to provide location information. It measures the magnetic field using magnetometers and processes the data to determine position. This allows accurate and consistent location data in areas like buildings, tunnels, or urban canyons where GPS signals are weak. The system compensates for magnetic anomalies and drifts using known maps and models of the magnetic field.

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Aiding data processing to increase reliability and availability of positioning, navigation, and timing (PNT) information using signals of opportunity (SoOPs) that are not primarily intended for PNT. The technique involves supplementing SoOP signals with additional information called aiding data to compensate for the fact that SoOPs are not optimized for PNT. The aiding data can be derived from sources other than the SoOPs themselves. By processing the SoOP signals along with the aiding data, it improves the accuracy and reliability of PNT estimates when traditional PNT sources like GPS are unavailable or degraded.

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Route navigation system that switches to SLAM-based positioning when GPS is unreliable. It uses SLAM (simultaneous localization and mapping) to navigate when GPS positioning becomes inaccurate. The system initially uses GPS for augmented reality navigation. When GPS positioning accuracy degrades, it converts SLAM coordinates into calculated geographic coordinates and continues augmented reality navigation using the SLAM-based coordinates. This avoids wrong route indications and improves accuracy when virtual and real scenes are combined.

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Navigation receiver that can estimate position, velocity, and time using fewer than four transmitters, like GPS satellites. The receiver can calculate position with just three transmitters if it knows the receiver's velocity. With two transmitters and known clock drift, it can estimate position. This enables positioning when some transmitters are missing or obstructed, or for hybrid systems with fewer satellites. The technique combines ranging and Doppler equations to solve for position with fewer transmitters.

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Improving the accuracy of precise GPS positioning in challenging environments by dynamically filtering GPS measurements based on quality checks. The method involves comparing the standard deviation of pre-fit residuals for different GPS measurement types like pseudorange, carrier phase, and Doppler. If certain measurement types exceed a threshold, it modifies how the positioning engine processes those measurements. This can exclude measurements, deweight them, or increase their uncertainty to account for poor quality. By adaptively filtering based on measurement quality, it reduces the impact of erroneous measurements in challenging GPS environments without needing extra sensors.

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