Honeywell International Inc.

Other

Wesley J Hawkinson

Robert C McCroskey

Kelvin M Korver

Donovan Boedigheimer

Yunqian Ma

Milos Sotak

Jindrich Dunik

Wayne A Soehren

Ryan Douglas Ingvalson

Kevin Sweeney

Milos Vesely

Paul Samanant

Michael Ray Elgersma

Rida M Hamza

Lawrence C Vallot

Brian W Schipper

Randolph G Hartman

Kenneth Steven Morgan

Charles T Bye

Steven J Sanders

Matthew Edward Lewis Jungwirth

John B McKitterick

John Burt McKitterick

Sorin Mosor

Tom Rolfer

Robert C Mccroskey

Lawrence Charles Vallot

Using geo-mapping data and only at least one inertial sensor, a Bayesian estimator uses at least one element of an uncorrected inertial navigation vector, of a moveable object on a surface, to estimate inertial navigation error information including corresponding statistical information. The estimated inertial navigation error information is used to error correct the uncorrected inertial navigation vector. The Bayesian filter also predicts inertial navigation error information, including corresponding statistical information, for a future time instance when a next uncorrected inertial navigation vector will be obtained.

Apparatus and method for terrain aided navigation using inertial position

A method is provided. The method comprises: initializing a point mass filter; initializing the one or more Bayesian filters; obtaining measurement data associated with a horizontal position on a surface; obtaining measurement data of a horizontal position and a velocity; obtaining geo-mapping data; estimating, with the point mass filter, the horizontal position on the surface based upon the geo-mapping data and the measurement data; estimating, with the one or more Bayesian filters, remaining state parameters based upon an output of the point mass filter and the measurement data; predicting, with the point mass filter, an a priori horizontal position, on a surface for a future time when the next measurement data will be obtained; and predicting, with the one or more Bayesian filters, an a priori remaining state parameters for a future time when the next measurement data will be obtained.

Apparatus and method for data-based referenced navigation

An optical system comprises a pair of Risley prisms positioned along an optical axis to receive a light beam from a field of view, wherein at least one of the Risley prisms is rotatable, transverse to the optical axis, with respect to the other of the Risley prisms. At least one lens is positioned along the optical axis to receive the light beam from the pair of Risley prisms, with the at least one lens configured to focus the light beam. An optical detector array is positioned along the optical axis at an image plane, wherein the optical detector array receives the focused light beam on the image plane from the at least one lens. The optical system can be implemented as a light beam steering mechanism in a star tracker or celestial aided inertial navigation unit.

Risley prism based star tracker and celestial navigation systems

A system to detect spoofing attacks is provided. The system includes a satellite-motion-and-receiver-clock-correction module, a compute-predicted-range-and-delta-range module, a subtractor, and delta-range-difference-detection logic. The satellite-motion-and-receiver-clock-correction module periodically inputs, from a global navigation satellite system (GNSS) receiver, a carrier phase range for a plurality of satellites. The satellite-motion-and-receiver-clock-correction module outputs a corrected-delta-carrier-phase range for a current epoch to a first input of a subtractor. The compute-predicted-range-and-delta-range module outputs a predicted delta range to a second input of the subtractor. The predicted delta range is based on inertial measurements observed for the current epoch. The subtractor outputs a difference between the corrected-delta-carrier-phase range and the predicted delta range for the current epoch to delta-range-difference-detection logic. The delta-range-difference-detection logic determines if the difference exceeds a selected-range threshold. If the difference exceeds the selected-range threshold, the delta-range-difference-detection logic determines the GNSS receiver was spoofed in the current epoch.

Global navigation satellite system (GNSS) spoofing detection with carrier phase and inertial sensors

One embodiment is directed towards a method of navigating a body. The method includes determining a respective measured direction of each of a plurality of celestial objects with respect to the body based on an output of one or more star tracking sensors mounted to the body. Calculating an expected direction of at least one of the plurality of celestial objects with respect to the body based on a current navigation solution for the body. Calculating an updated navigation solution for the body based on the expected direction of the at least one celestial object, the measured direction of the plurality of celestial objects, and an output of one or more inertial sensors mounted to the body.

Navigating with star tracking sensors

Systems and methods for a limb strike detector are provided. In certain embodiments, a system for detecting limb strikes comprises an inertial measurement unit (IMU) that provides inertial measurements; and a processing unit that receives the inertial measurements from the IMU. In certain embodiments, computer readable instructions direct the processing unit to determine multiple local vertical acceleration samples, wherein each local vertical acceleration sample in the multiple local vertical acceleration samples defines an acceleration component along the local vertical axis and an associated time; integrate the multiple local vertical acceleration samples to determine multiple local vertical velocity samples; identify limb strike regions from the multiple local vertical velocity samples, wherein a limb strike region is associated with a local vertical velocity sample in the plurality of local vertical velocity samples; and determine limb strike times based on the identified limb strike regions and the multiple local vertical acceleration samples.

Systems and methods for a limb strike detector

A system and method for motion-based adaptive frequency estimation of a Doppler sensor is provided. The system comprises a Doppler sensor configured to output a digitized Doppler data signal, and a Doppler velocity estimation module operatively coupled to the Doppler sensor to receive the Doppler data signal. An inertial navigation system is operatively coupled to the Doppler velocity estimation module, and one or more inertial sensors is operatively coupled to the inertial navigation system. The inertial sensors are configured to transmit inertial navigation data to the inertial navigation system. The Doppler velocity estimation module calculates a speed or velocity estimate based on the Doppler data signal and the inertial navigation data. The speed or velocity estimate is then transmitted to the inertial navigation system.

Motion-based adaptive frequency estimation of a doppler velocity sensor

Systems and methods for navigation using cross correlation on evidence grids are provided. In one embodiment, a system for using cross-correlated evidence grids to acquire navigation information comprises: a navigation processor coupled to an inertial measurement unit, the navigation processor configured to generate a navigation solution; a sensor configured to scan an environment; an evidence grid creator coupled to the sensor and the navigation processor, wherein the evidence grid creator is configured to generate a current evidence grid based on data received from the sensor and the navigation solution; a correlator configured to correlate the current evidence grid against a historical evidence grid stored in a memory to produce displacement information; and where the navigation processor receives correction data derived from correlation of evidence grids and adjusts the navigation solution based on the correction data.

Systems and methods for navigation using cross correlation on evidence grids

Embodiments of the present invention provide improved systems and methods for estimating N-dimensional parameters while sensing fewer than N dimensions. In one embodiment a navigational system comprises a processor and an inertial measurement unit (IMU) that provides an output to the processor, the processor providing a navigation solution based on the output of the IMU, wherein the navigation solution includes a calculation of an n-dimensional parameter. Further, the navigational system includes at most two sensors that provide an output to the processor, wherein the processor computes an estimate of an n-dimensional parameter from the output of the at most two sensors for bounding errors in the n-dimensional parameter as calculated by the processor when the trajectory measured by the IMU satisfies movement requirements, wherein 'n' is greater than the number of the at most two sensors.

Estimation of N-dimensional parameters while sensing fewer than N dimensions

A navigation system includes host and remote units. Host unit includes positioning device to determine absolute position/orientation of host unit; first communication device to communicate signals; first processor; and first memory. Remote unit includes second communication device to receive signals from first communication device; second processor; and second memory. First or second processor compares first aspects of known pattern with second aspects of image of captured pattern positioned on surface at either host unit or remote unit. First or second processor determines relative position/orientation of remote unit relative to host unit based on comparison of first aspects and second aspects. First or second processor determines absolute position/orientation of remote unit based on relative position/orientation of remote unit relative to host unit and absolute position/orientation of host unit. Either absolute position/orientation of host unit or absolute position/orientation of remote unit are communicated from host unit to remote unit.

Using structured light to update inertial navigation systems

A navigation device is provided herein comprising an inertial measurement unit (IMU), a camera, and a processor. The IMU provides an inertial measurement to the processor and the camera provides at least one image frame to the processor. The processor is configured to determine navigation data based on the inertial measurement and the at least one image frame, wherein at least one feature is extracted from the at least one image frame based on the navigation data.

Camera and inertial measurement unit integration with navigation data feedback for feature tracking

A method of stabilizing heading in an inertial navigation system includes operating an inertial measurement unit comprising horizontal-sensing elements and off-horizontal-sensing elements while the inertial measurement unit is in a first orientation, calibrating the horizontal-sensing elements of the inertial measurement unit based on horizontal aiding measurements, forward-rotating the inertial measurement unit by a selected-rotation angle about a horizontal-rotation axis so that the inertial measurement unit is oriented in a second orientation, operating the forward-rotated inertial measurement unit while the inertial measurement unit is in the second orientation, and calibrating the rotated off-horizontal-sensing elements based on horizontal aiding measurements while the inertial measurement unit is in the second orientation. When the inertial measurement unit is in the first orientation, the horizontal-sensing elements are oriented in a horizontal reference plane. When the inertial measurement unit is in the second orientation, the off-horizontal-sensing elements are oriented in the horizontal reference plane.

Heading stabilization for aided inertial navigation systems

A method for navigating underwater is disclosed. The method uses a navigation system to project a first velocity measurement along one or more signal beams having a second velocity measurement, where the second velocity measurement is related to at least one of the one or more signal beams. The method determines a position and location of an object associated with the navigation system based on a prediction of at least the second velocity measurement, and the navigation system is adjusted to perform within a prescribed measurement range based on a covariance of the first and second velocity measurements. The performance adjustments made in determining the position and location of the object are operable independent of the navigation system maintaining each of the signal beams due to one or more external environmental conditions.

Methods and systems for underwater navigation

Systems and methods for terrain contour matching navigation are provided. In one embodiment, a method for terrain contour matching navigation comprises: receiving at least one sample point representing the position of an aircraft, the at least one sample point including a horizontal position and an altitude sample; correlating a first sample point of the at least one sample point across a reference basket array having a plurality of elements; determining a correlation quality; when the correlation quality does not achieve a pre-determined quality threshold, performing at least one additional correlation of an additional sample point of the at least one sample point across the reference basket array; and when the correlation quality does achieve a pre-determined quality threshold, calculating a position error based on the correlating of the first sample point and any additional correlations of any additional sample points.

Systems and methods for a terrain contour matching navigation system

Systems and methods for reducing vibration-induced bias errors in inertial sensors are disclosed. A system for reducing bias errors in an inertial sensor operating within an environment may include a vibration detector for sensing vibration changes within the environment proximate to the inertial sensor, and a Kalman filter for computing an estimate of the navigational error produced by the inertial sensor due to a vibration-induced bias shift detected by the vibration detector. The vibration detector can be configured to measure an accelerometer output of the inertial sensor over a Kalman filter cycle, and then use the standard deviation of such output to obtain a statistical measure of the vibration within the environment. In some embodiments, the inertial sensor may include an inertial measurement unit (IMU) connected to an error compensation unit and strapdown navigator, each of which can be fed navigation corrections determined by the Kalman filter.

Growing community of inventors

Chanhassen, MN, United States of America

Wesley J Hawkinson