Rockwell Collins, Inc.

Ramachandra J Sattigeri

Vladislav Gavrilets

Christopher M Boggs

William T Kirchner

Arjun Passi

James H Doty

Bobby D Foote

Christopher L George

Maxime M Gariel

Paxton Alsgaard

Sami S Mina

A system and method established and maintains precision relative position, navigation, and timing (PNT) across a network of at least four mutually connected mobile platforms. In embodiments, a key (e.g., advantaged, absolute positioning capable) node of the network determines its pressure altitude and inertial state relative to its platform reference frame and receives inertial state and pressure altitude data from each neighboring node (in exchange for its own) to estimate the relative position and orientation of each neighbor node in its platform frame. The key node performs ranging to each neighboring node, and the neighboring nodes additionally range between each other and exchange ranging data with the key node. By correcting position and orientation estimates via ranging data, the key node determines and maintains extended relative PNT (e.g., in GPS-denied areas), which relative PNT solution is distributed across all network nodes.

System and method for maintaining cooperative precision navigation and timing (PNT) across networked platforms in contested environments

A system and method for identifying a safe path in a partially unknown environment in a time critical fashion includes identifying long range paths that avoid known obstacles. Short range paths within a receding horizon are identified corresponding to the long range paths, the short range paths avoiding known obstacles and unknown regions. Each short range path is broken into segments based on subsequent control operations or timing, and abort procedures are identified corresponding to each segment to enforce time critical availability of a safe action. Short range paths are excluded unless abort procedures can be identified for subsequent segments.

Time-critical obstacle avoidance path planning in uncertain environments

A system in a mobile platform creates a three-dimensional world model from sets of volume elements organized into sets of volume elements of increasing size and converted into an octree data structure. The system receives a request for a map of a specific, limited region and at a specific, limited level of detail and produces a reduced three-dimensional world model to conform to the limited region and level of detail. The reduced map is transmitted to the requesting platform and periodically updated. The map may be reduced by categorizing the lowest level of volume elements according to the worst case of any constituent volume elements. Alternatively, the system may indicate in the lowest level of volume elements whether the receiving platform would benefit from an increased level of detail.

Efficient transfer of dynamic 3D world model data

Systems and methods configured to adaptively control input sequences that are used to conduct qualification tests of simulators are disclosed. An input sequence may be adaptively controlled by: providing the input sequence to a simulator configured to simulate operations of a device; comparing output of the simulator to a set of recorded operation data obtained from the device; adaptively modifying the input sequence within specified bounds to produce a modified version of the input sequence to reduce one or more differences that exist between the output of the simulator and the set of recorded operation data; and providing the modified version of the input sequence to the simulator to simulate operations of the device.

Adaptive control for simulator input generation

A system and related method for hybrid headtracking receives head-referenced pose data from a head-mounted IMU and platform-referenced or georeferenced position and orientation (pose) data from a platform-mounted IMU, determines error models corresponding to uncertainties associated with both IMUs, and performs an initial estimate of head pose relative to the platform reference frame based on the head-referenced and platform-referenced pose data. A numerically stable UD factorization of the Kalman filter propagates the estimated head pose data forward in time and corrects the initial head pose estimate (and may correct the error models associated with the head-mounted and platform-mounted IMUs) based on secondary head pose estimates, and corresponding error models, received from an optical or magnetic aiding device. The corrected head pose data is forward to a head-worn display to ensure high accuracy of displayed imagery and symbology.

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Ashburn, VA, United States of America

Ramachandra J Sattigeri