Drift Reduction in UAV Inertial Navigation Systems

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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