Drift Reduction in UAV INS

Determining accuracy of smoothed navigation solutions using filtered resets, where a navigation system generates an un-smoothed navigation solution based on inertial and aiding device measurements, calculates a smoothed navigation error estimate, and determines whether to provide a smoothed filter reset based on the smoothed and un-smoothed error estimates.

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A hybrid inertial navigation system (INS) that combines conventional inertial measurement units (IMUs) with light-pulse atom interferometer (LPAI) sensors. The system employs feedforward correction using the IMU data to compensate for platform motion and vibrations that limit LPAI performance. The correction enables dynamic operation of LPAI sensors under high-dynamic conditions, achieving strategic-grade accuracy while maintaining laboratory-level performance.

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A navigation method for a vehicle that combines inertial measurements with position measurements from GPS and odometers using an invariant Kalman filter. The method determines kinematic variables such as orientation, velocity, and position, and their uncertainties, using inertial measurements during propagation phases. During updating phases, it incorporates position measurements from GPS and odometers to correct the kinematic variables and their uncertainties. The method uses a virtual observation function to enable the combination of inertial and position measurements, and employs a gain matrix that is dependent on the measurement.

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Inertial measurement unit (IMU) system for precision guidance in GPS-denied environments, comprising multiple IMUs with diverse sensor types and ranges, strategically packaged to provide high-bandwidth and full-coverage measurement of angular rate and linear acceleration. The system employs novel packaging and isolation techniques to enhance sensor survivability and performance in high-g shock environments, and incorporates a recalibration module to address sensor error and bias shift caused by gun launch events. The system also features a dynamic sensor configuration capability, enabling handoff between groups of IMUs with varying dynamic range and resolution characteristics to optimize performance in different flight regimes.

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Calibration of an Inertial Measurement Unit (IMU) on a guideway-mounted vehicle using an inclinometer or tilt sensor to measure tilt angles and compare them with IMU acceleration measurements, enabling automated correction of IMU orientation and ensuring accurate vehicle location tracking.

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A vibrating gyroscope that rejects error terms due to Euler and Centrifugal forces using a temporal differential sensing method. The method involves sampling signals from the same sensing element at peak velocity and opposite sign, and combining them to cancel out unwanted forces while retaining the Coriolis force component. This approach eliminates the need for spatial differential sensing and reduces the complexity and power consumption associated with increasing sensing element velocity and reducing distances.

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A method for estimating and compensating for a vehicle's motion using a LiDAR sensor, comprising: acquiring a point cloud for the vehicle; estimating the vehicle's motion using a motion unit; estimating the ground plane of the LiDAR sensor; and calibrating the vehicle's pose using an automatic calibrator. The method calculates motion information, ground plane information, and pose information, and compensates for drift and changes in sensor position relative to the vehicle.

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A method and system for constraining navigational drift in a munition caused by Inertial Measurement Unit (IMU) bias error during flight of the munition in a constellation of a plurality of munitions in a Global Positioning System (GPS) denied attack. Each munition determines its estimated position and covariance via its navigation system, shares its position and range to other munitions via datalink communication, and constrains navigational drift by compensating for IMU bias error using the shared position and range information.

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A method for resetting the attitude of a dead reckoning navigation system, particularly in indoor environments, by combining the system's own inertial measurements with precise position data from a relative positioning system such as ultra-wideband telemetry. The method enables accurate attitude adjustment over short distances, allowing for precise tracking of people or objects within buildings.

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A lightweight pose estimation system for autonomous vehicles that determines vehicle position and orientation using a combination of inertial, kinematic, and odometry sensors. The system generates a pose value by integrating sensor readings and can adjust measurements based on observed noise. It enables efficient and real-time pose estimation for autonomous maneuvers, particularly in emergency braking applications.

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Calibration of inertial measurement units (IMUs) in autonomous vehicles using high-definition maps to improve navigation accuracy. The system calibrates IMU measurements by comparing them to velocity data obtained from HD map localization, enabling precise determination of vehicle motion parameters and enhancing overall navigation performance.

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A system for providing location and guidance in GPS-denied environments using a multi-IMU package that combines low-accuracy IMUs to achieve high-accuracy results. The system comprises a miniature multi-IMU package with multiple low-accuracy IMUs, a processor, and a sensor fusion algorithm that synchronizes and combines the IMU signals to provide a high-performance navigation solution. The system exhibits an angular random walk of less than 0.09°/√hour, enabling accurate location and guidance in GPS-denied environments.

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Multi-IMU navigation solution that improves the accuracy and high resolution of navigation in a global positioning system (GPS) denied and/or degraded environment. The solution includes an electronic board comprising an upper surface, a lower surface and a plurality of inertial measurement units (IMUs) mounted on at least one of the surfaces, each IMU having a signal and comprising at least one three-axis accelerometer and/or at least one three-axis gyroscope, the IMUs adapted to be coupled together via firmware, a processor adapted to receive the signal from each IMU, and an algorithm comprised in the processor, the algorithm adapted to synchronize the signals from each of the IMUs, calculate a bias and a drift in the signal of each IMU, and to provide a guidance metric representative of the absolute or relative location of a munition guided by the guidance system and based on the signals of each of the IMUs.

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MEMS inertial sensors with improved sensing accuracy through the use of kinematic linkages that reduce stress and nonlinearity errors. The linkages, comprising pivoting bars and dynamic pivots, connect the proof mass to the drive or sense structure, enabling controlled motion while mitigating sources of error such as quadrature, shear and normal stress, and cubic stiffness.

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A system and method for reducing vertical reference unit (VRU) unreferenced heading drift error in vehicles, particularly in applications where precise heading information is critical. The system employs a two-stage bias cancelation approach to mitigate the effects of gyroscope bias in inertial measurement units (IMUs). The first stage involves static detection and initial bias correction during startup, while the second stage continuously monitors and updates the bias during in-run operation. This approach enables accurate and stable heading information even in applications where traditional IMU-based heading solutions are insufficient.

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System for determining the pose of an autonomous vehicle using a consensus-based approach to combine inertial measurement unit (IMU) data from multiple sensors. The system identifies a primary IMU based on consensus determination and uses its measurements in conjunction with other input signals to determine the vehicle's pose. The system also monitors input signals for staleness and implausibility, and dynamically selects a state update algorithm based on the monitoring signal and consensus determination.

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Autonomous vehicle control system that determines the state of a vehicle by fusing data from multiple sensors, including inertial measurement units (IMUs), and perception sensors. The system identifies and compensates for IMU data offsets by comparing data from multiple IMUs or by correlating IMU data with perception sensor data.

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System and method for controlling movement of a machine using communicatively coupled inertial measurement unit (IMU) modules mounted on machine components. The system includes a plurality of IMU modules, each comprising an IMU, processing device, and state estimator, communicatively coupled to a communication bus. The IMU modules receive orientation and motion measurements, fuse the data, and determine output orientation and motion data for machine components. The system uses a Kalman filter associated with each IMU to estimate positions and orientations of machine components, enabling real-time control of machine movement.

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A method of operating a vehicle that improves navigation accuracy by combining data from multiple sources with statistical weights based on their reliability. The method collects navigation parameters from sensors, GPS, and inertial systems, determines their uncertainties, and assigns weights to each parameter based on its reliability. A navigational solution is then formed by blending the weighted parameters using a Kalman filter, providing an optimized navigation solution with overall uncertainty estimates.

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An inertial navigation device error correction system for aerial vehicles enables autonomous flight even in GPS-denied environments. The system uses inter-vehicle communication to calculate the position of one vehicle relative to another, and then uses the differences between the calculated and measured angles to correct the inertial navigation device's bias errors. This approach enables accurate position and attitude determination even when one or more vehicles are malfunctioning or intentionally providing false data.

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A navigation method for a vehicle equipped with a navigation device comprising a processing unit, three accelerometers, three gyroscopes and a device for measuring a position of the navigation device. The method includes determining a priori kinematic variables of the navigation device, determining respective current values of kinematic variables of the navigation device and a current uncertainty matrix representative of an uncertainty of the respective current values of the variables, and determining a correction and updating the respective current values of the kinematic variables using a Kalman filter.

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Inertial navigation system using atomic interferometer sensors to provide precise measurements of linear acceleration, rotation, and gravity gradients, enabling autonomous navigation when GNSS signals are unavailable. The system comprises multiple atomic interferometer inertial sensors and gravity gradiometers that provide raw measurements, which are then processed to determine position.

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A method and apparatus for yaw fusion in aircraft, particularly for unmanned aerial vehicles (UAVs), that improves the stability and precision of yaw estimation through secondary complementary filtering. The method combines data from multiple sensors, including magnetometers, inertial measurement units (IMUs), and global positioning systems (GPS), to correct yaw angular velocity and estimate the final yaw angle. The secondary filtering stage compensates for errors introduced by primary filtering, ensuring accurate and stable yaw estimation even during long-term flight or prolonged yaw-angle maneuvers.

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A navigation system for GPS-denied environments that combines inertial measurement unit (IMU) data with gravitational anomaly measurements from a vertical accelerometer integrated into the IMU. The system uses a gravity map to correct for gravitational anomalies and generates navigation corrections based on the IMU data, gravity map data, and estimated gravitational anomalies. The system can be used for aircraft, surface vehicles, and sub-surface vehicles.

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Navigation system that maintains state information of a vehicle or object in motion relative to a geocenter in an Earth Centered Earth Fixed (ECEF) frame of reference, using an inertial measurement unit (IMU) to track accelerations and angular orientation changes, and performing navigation functions based on the ECEF state.

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Inertial navigation system using atomic interferometer sensors to provide precise measurements of acceleration, rotation, and gravity gradients, enabling autonomous navigation in GNSS-denied environments. The system comprises multiple atomic interferometer inertial sensors and gravity gradiometers that provide raw measurements, which are processed to determine position.

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A multi-IMU system for providing location and guidance in GPS-denied environments, comprising a miniature package housing multiple low-accuracy IMUs that are fused together to achieve high-accuracy navigation. The system employs a sensor fusion algorithm to combine data from the individual IMUs, enabling the creation of a single high-performance IMU that rivals tactical-grade devices. The system also features a dynamic sensor configuration that can switch between high-dynamic-range/low-resolution and low-dynamic-range/high-resolution modes based on environmental conditions.

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A navigation system for autonomous vehicles that treats an inertial measurement unit (IMU) as the primary sensor, with other sensors providing correction. The IMU operates at lower latency than other sensors, enabling precise localization within 10 cm accuracy and 10 ms latency. The system combines data from the IMU, perception sensors, and GPS, with the IMU's data treated as primary and corrected by the other sensors. This approach provides enhanced localization compared to traditional systems that rely on GPS or perception sensors as the primary sensor.

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Compensating for oscillating attitude disturbances in aerial vehicles caused by rotating payloads, such as LiDAR systems, by predicting and actively counteracting the disturbances through real-time modeling and control of the vehicle's thrusters. The system determines vehicle and payload parameters, calculates a preferred orientation, and generates corrective inputs based on actual orientation feedback to maintain stable flight.

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Alignment system for aircraft countermeasures that uses gyroscopes to determine the relative orientation of electro-optical sensors mounted on a flexible aircraft frame. The system employs a Kalman estimator to converge on the sensor alignment, utilizing a low-pass filter to remove body flex frequencies and pre-rotating INS measurements to improve steady-state accuracy. The system enables precise threat detection and tracking by mapping sensor detections into a common reference frame maintained by a central processor.

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Method for aiding navigation of a mobile carrier using an inertial navigation unit that estimates the carrier's state by iteratively linearizing a non-linear system around the current estimate, propagating the previous state, and backpropagating corrections from the current state to the propagated state. The method uses a Kalman filter and stochastic cloning to estimate the carrier's state from inertial and external measurements, and is particularly suited for high-precision inertial navigation systems.

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System and method for controlling movement of a machine using communicatively coupled inertial measurement unit (IMU) modules mounted on machine components. The system includes a plurality of IMU modules, each comprising an IMU, processing device, and state estimator, that form a mesh network communicatively coupled to a communication bus. The IMU modules receive orientation and motion measurements, fuse the signals, and determine output orientation and motion data for the machine components. The system uses the fused data to estimate real-time position, velocity, and acceleration of the machine components, and applies this information to control the machine's movement.

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A navigation system that improves the accuracy of inertial navigation systems (INS) by iteratively estimating and correcting INS errors using terrain-based navigation data and a stored gravity map. The system receives position estimates from both the INS and terrain-based navigation unit, determines gravity information at the current position, and uses this information to calculate a gravity-corrected position estimate. The INS error state is then updated based on the corrected estimate and the terrain-based position estimate, enabling continuous refinement of the INS position estimate.

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Autonomous vehicle control system that determines the state of a vehicle by fusing data from multiple sensors, including inertial measurement units (IMUs), and perception sensors. The system identifies and compensates for IMU data offsets by comparing data from multiple IMUs or by correlating IMU data with perception sensor data.

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A system for providing location and guidance in GPS-denied environments using a combination of low-accuracy inertial measurement units (IMUs). The system comprises multiple IMUs mounted on a single electronics board, each with accelerometers and gyroscopes, which are coupled together via firmware. A processor receives the signals from each IMU and uses an algorithm to synchronize the signals, calculate biases and drifts, and provide a guidance metric representing the absolute or relative location of a munition. The system achieves high-accuracy location and guidance by leveraging the redundancy and diversity of the multiple IMUs, even when individual IMUs have low accuracy.

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Integrated flight control and navigation system for unmanned aerial vehicles (UAVs) that combines MEMS inertial measurement unit data with GPS/BD/GLONASS receiver data and RTK navigation system to improve positioning accuracy and enable autonomous flight control.

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Alignment method for a carrier device with an inertial measurement unit, comprising a plurality of inertial sensors and an embedded computer executing the method. The method estimates the carrier device's state, comprising speed and orientation, using a simplified mode that enables processing by an invariant Kalman filter. The simplified mode expresses each speed in a work reference frame translated relative to the inertial reference frame, with the origin moving along an inertial reference trajectory close to the carrier device's path. The method iteratively updates the state estimate using the Kalman filter, without estimating sensor bias during propagation and updating steps.

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Method and device for estimating a state associated with a mobile body, including an attitude of the mobile body in the presence of narrowband noise. The method includes obtaining measurements from a plurality of measuring devices associated with the mobile body, filtering the measurements using a noise filter, predicting a second estimate of the state based on a first estimate and a plurality of models, and updating the second estimate based on the filtered measurements to obtain a third estimate.

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Electronic sensing of angle offsets from a gravity vector in vehicles, employing a dynamic weighting module that combines IMU and vehicle control system data to estimate vehicle attitude. The module receives predicted acceleration information from the vehicle control system and dynamically weights IMU data based on accelerometer estimates of the gravity vector. The system includes motion detection algorithms, gyroscope bias compensation, and Kalman filter-based inclination estimation to improve attitude measurement accuracy.

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An aerial vehicle system that uses an inertial measurement unit (IMU) to sense attitude (orientation) and corrects for drift error. The system includes an IMU with gyroscopes and accelerometers, and an attitude adjustment module to generate an attitude adjustment value. An attitude correction determination module compares the estimated attitude from the accelerometers with the adjusted sensed attitude and generates an attitude correction factor. A scaling factor module uses stability and gyroscope saturation signals to generate a scaling factor. An attitude estimate alteration module combines the correction factor and scaling factor to generate the final attitude adjustment value that compensates for IMU drift error.

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A navigation system for autonomous vehicles that integrates radar measurements with inertial sensor data to provide accurate positioning in environments where GPS signals are unreliable. The system uses a nonlinear state estimation technique that incorporates a radar measurement model to update the vehicle's state estimate, leveraging both the short-term accuracy of inertial sensors and the long-term consistency of radar measurements. The radar measurement model can take various forms, including range-based, nearest object likelihood, map matching, and closed-form models, each of which can be used to correlate radar data with map information to improve navigation accuracy.

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Positioning system for vehicles, ships, and aircraft that combines data from multiple inertial measurement units and common sensors, such as GPS and altimeters, to provide improved accuracy and reliability. The system uses a novel architecture where each navigation filter operates independently but shares common information, and the estimates are fused using a mean estimate approach that optimizes the state vector. The system also includes an error detection module to identify sensor failures or malfunctions.

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Accurate guidance and navigation system for munitions that can operate in GPS denied or degraded environments. The system uses a multi-IMU setup with multiple low-cost inertial measurement units (IMUs) combined to provide high accuracy and resolution location and guidance. The IMUs are fused together using algorithms to compensate for individual sensor errors and provide a single, more accurate guidance metric. This allows precise guidance in environments where standalone IMUs may fail due to vibration, shock, or jamming.

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