The Charles Stark Draper Laboratory, Inc.

Bae Systems Information and Electronic Systems Integration Inc.

Simone B Bortolami

Juha-Pekka J Laine

Stephen P Smith

Joseph M Kinast

Helen F Webb

Benjamin F Lane

Jeffrey A Korn

Robin Mark Adrian Dawson

Gregory P Blasche

Matthew T Jamula

Blaise Grayson Morton

Paul A Bohn

Marvin A Biren

Eric T Hoke

Daniel M Meiser

Charles Arant

Stephen P Delmarco

Larry H Mack

Timothy J McCarthy

Frank R Stolle

Benjamin J Bresler

A method of determining a point-of-impact of a ballistic projectile comprising: within a predetermined site using a weapon comprising a barrel, a camera, and an IMU configured to provide data concerning barrel attitude: detecting a firing event associated with the weapon; obtaining metadata associated with the weapon; determining estimated pointing angles of the weapon; obtaining a reference image of the site; culling portions of the reference image not associated with the estimated pointing angles, creating a culled reference image; projecting the culled reference image onto an image plane; obtaining an image from the weapon-mounted camera, the image being centered on the pointing direction weapon at the time of firing; registering the image from the weapon-mounted camera with the culled reference image; and calculating the true pointing angles of the weapon based on the alignment of the image obtained by the weapon-mounted camera with the culled reference image.

High-precision infantry training system (HITS)

A weapon-mountable smart optic comprising: a time reference configured to output a signal comprising a periodically-repeating feature and time metadata and comprising a first oscillator; at least two sensors configured to gather data, each comprising secondary oscillators; and at least one processor in communication with each of the at least two sensors; wherein each of the at least two sensors is in operative communication with the time reference and is configured to associate an edge of the periodically-repeating signal with a time conveyed by the time metadata, and wherein each of the at least two sensors is configured to gather data, associate time metadata with the gathered data, and to send the gathered data with time metadata to the at least one processor, and wherein the at least one processor is configured to fuse the data gathered by each of the at least two sensors.

Time synchronization of optics using power feeds

Techniques are provided for image-based estimation of the orientation of an airborne platform. A methodology implementing the techniques according to an embodiment includes rotating an image, provided by a camera on the platform, by a selected rotation angle. The method also includes computing an intensity gradient image based on the rotated image. The method further includes extracting a left quarter segment and a right quarter segment of the intensity gradient image. The method further includes flipping one of the extracted segments about a vertical axis through that segment and comparing the non-flipped segment to the flipped segment to generate a matching score. The comparison may be based on a correlation between the flipped segment and the non-flipped segment. The process may be repeated for additional rotation angles and the rotation angle associated with the highest matching score is selected as the estimated orientation angle of the platform.

Image based estimation of aerial platform orientation in roll and pitch

An ensemble of motion sensors is tested under known conditions to automatically ascertain instrument biases, which are modeled as autoregressive-moving-average (ARMA) processes in order to construct a Kalman filter. The calibration includes motion profiles, temperature profiles and vibration profiles that are operationally significant, i.e., designed by means of covariance analysis or other means to maximize, or at least improve, the observability of the calibration model's structure and coefficients relevant to the prospective application of each sensor.

Fused sensor ensemble for navigation and calibration process therefor

A probabilistic landmark navigation system is arranged to obtain from one or more image sensors one or more terrain images of a target terrain to be traversed by a vehicle. The terrain images are characterized by multiple individually non-distinctive terrain features without distinctively identifiable landmark features. The non-distinctive terrain features in the one or more terrain images are compared to map database information to make a non-temporal probabilistic determination of absolute location coordinates to associate with the non-distinctive terrain features. Then a navigation path is determined for the vehicle across the target terrain based on the absolute location coordinates.

Probabilistic landmark navigation (PLN) system

Methods for use in celestial navigation system use multiple star observations from an unknown observer location using an observer camera with an observational field of view along a boresight axis and a focal plane perpendicular to the boresight axis. An observer local ECI coordinate system is determined from the star observations. Then for each of multiple observable objects in orbit around the Earth, multiple optical angular measurements relative to stars are performed over time to estimate its orbital ellipse, and its focus at the center of the Earth. From the multiple focus estimates, a position of the center of the Earth is estimated in the observer local coordinate system without use of object ephemeris data. From the estimated position of the center of the Earth, a radius vector to the observer is determined that represents the estimated observer location in Earth centered Earth fixed coordinates.

Ephemerides-free celestial navigation

A visual navigation system includes a compass configured to orient a user in a heading direction, an image sensor configured to capture a series of successive navigation images in the heading direction, one or more of the navigation images having at least two reference markers, data storage memory configured to store the series of successive navigation images, a navigation processor configured to identify at least one principal marker and at least one ancillary marker from the at least two reference markers, the principal marker positioned within a principal angle and the ancillary marker positioned within an ancillary angle, which is greater than the principal angle, and to determine heading direction information based on a position of the at least one principal marker and/or the at least one ancillary marker in the successive navigation images, and a user interface configured to provide the heading direction information to the user.

Camera-based heading-hold navigation

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Belmont, MA, United States of America

Simone B Bortolami