Honeywell International Inc.

Honeywell G.m.b.h.

Open Invention Network LLC

Wayne A Soehren

Charles T Bye

Lawrence C Vallot

Wesley J Hawkinson

Christopher A Lund

Michael Ray Elgersma

Rida M Hamza

Brian W Schipper

Randolph G Hartman

Ryan Douglas Ingvalson

Mario B Ignagni

Kailash Krishnaswamy

Ross Merritt

Mahesh K Jeerage

Charles L Keyes

An example pointing system includes a sensor that measures change in angular position, a first GNSS antenna, and a second GNSS antenna mounted to a rigid body that is removable from the pointing system after calibration of the sensor. The GNSS antennas have a fixed, known baseline. The pointing system includes at least one GNSS receiver with first and second RF inputs respectively coupled to the GNSS antennas. The at least one GNSS receiver includes respective paths to process GNSS signals received from the first and second RF inputs. The pointing system includes at least one processor, communicatively coupled to the sensor and receiver, configured to: determine initial attitude of the pointing system based on the processed GNSS signals; calibrate the sensor using the determined initial attitude; determine a pointing solution for the pointing system based on measurements from the calibrated sensor without GNSS signals from second GNSS antenna.

Pointing system alignment using GNSS attitude determination with removable GNSS antenna

A system and method for collaborative navigation is provided. The system comprises a first mobile unit, at least one inertial measurement unit on the first mobile unit, and at least one environment sensor on the first mobile unit. A navigator module in the first mobile unit is configured to receive inertial data from the inertial measurement unit. An object characterization module is configured to receive sensor data from the environment sensor and a navigation solution from the navigator module. A common object geo-locator module is configured to receive a first set of descriptors from the object characterization module and a second set of descriptors from another mobile unit. A data association module is configured to receive common descriptors from the common object geo-locator module. The first mobile unit is configured to operatively communicate with one or more additional mobile units that are configured for collaborative navigation with the first mobile unit.

System and method for collaborative navigation

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

Systems and methods for differential altitude estimation utilizing spatial interpolation of pressure sensor data are provided. In one embodiment, a method for mobile navigation comprises: measuring a horizontal location of a mobile navigation unit to generate two-dimensional horizontal coordinate data; measuring a barometric pressure at the mobile navigation unit with a sensor to obtain local pressure data; processing information representative of pressure data derived from a network of a plurality reference stations to obtain a correction factor; performing a calculation using the two-dimensional horizontal coordinate data, the local pressure data, and the correction factor to calculate an altitude coordinate; and determining an altitude of the mobile navigation unit from the altitude coordinate.

Systems and methods for differential altitude estimation utilizing spatial interpolation of pressure sensor data

At least one inertial sensor is configured to sense movement of a telephone (or other portable device such as a personal digital assistant, portable computer, portable game device, portable audio player, or portable video player). Information derived at least in part from data output by the inertial sensor is used as input to software executing on the telephone.

Telephone or other portable device with inertial sensor

A relative navigation system and method are disclosed. The relative navigation system includes a first sensor unit responsive to a motion of a first position, a second sensor unit responsive to a motion of a second position, and a first processing unit associated with at least one of the first sensor unit and the second sensor unit and communicatively coupled to the first sensor unit and the second sensor unit. The first processing unit is configured to generate relative navigation solution information associated with first sensor unit information and second sensor unit information.

System and method for high accuracy relative navigation

A relative navigation system including a first unit responsive to the motion of a first position, a second unit responsive to the motion of a second position and a processing unit that generates a relative navigation solution as a function of first unit information and second unit information. The generated relative navigation solution is indicative of at least one of: a relative position vector of the second position relative to the first position, a relative velocity of the second position relative to the first position, and a relative attitude of the first unit at the first position relative to the second unit at the second position.

Method and apparatus for high accuracy relative motion determination using inertial sensors

A navigation system used by a user includes a navigation unit and a portable device. The navigation unit generates navigation-related output. The navigation unit transmits the navigation-related output to the portable device over a wireless communication link. The portable device receives the navigation-related output and uses at least a portion of the navigation-related output for processing performed thereby. In one embodiment, the portable device includes an input interface to receive user input from a user of the portable device. In such an embodiment, the portable device transmits the navigation-related input to the navigation unit over the wireless communication link, and the navigation unit uses at least a portion of the navigation-related input to generate the navigation-related output.

Personal navigation device for use with portable device

An inertial navigation system includes a navigation component, such as an inertial measurement unit, a processor in communication with the navigation component, and a filter embedded in computer code implemented within the processor. The filter includes a model of the navigation component, and one or more parameters of the model are configured after startup of the inertial navigation system based upon performance characteristics of the individual navigation component included in the inertial navigation system.

Navigation component modeling system and method

A personal navigation system including one or more sensors that sense motion of a human and output signals corresponding to the motion of the human and a human-motion-classification processing block that receives sensor data from the one or more sensors. The human-motion-classification processing block includes a Kalman filter processing block, an inertial navigation processing block, and a motion classification processing block. The Kalman filter processing block executes a Kalman filter that provides corrections to the motion classification processing block. The inertial navigation processing block receives input sensor data from the sensors and outputs a navigation solution. The motion classification processing block executes a motion classification algorithm that implements a step-time threshold between two types of motion, identifies a type of motion based on the received sensor data, and outputs a distance-traveled estimate to the Kalman filter processing block based on the identified type of motion.

Motion classification methods for personal navigation

A navigation system includes an inertial navigation unit. The navigation system also includes a Kalman filter that generates corrective feedback for use by the inertial navigation unit. The Kalman filter generates the corrective feedback as a function of at least one of GPS/DGPS information, sensor information, user input, terrain correlation information, signal-of-opportunity information, and/or position information output by a motion classifier.

Personal navigation using terrain-correlation and/or signal-of-opportunity information

A system for providing connectivity to co-located instruments includes a processor for receiving and processing GPS data and IMU data, a GPS receiver antenna operable to supply the GPS data to the processor, and an IMU, co-located with the GPS receiver antenna, operable to provide the IMU data to the processor. A single cable is provided between the processor and co-located equipment and, by using filtering mechanisms, the single cable is operable to simultaneously supply DC power to the IMU and to transmit the GPS data and the IMU data to the processor.

System and method for multiplexing and transmitting DC power, IMU data and RF data on a single cable

A navigation system for mounting on a human. The navigation system includes one or more motion sensors for sensing motion of the human and outputting one or more corresponding motion signals. An inertial processing unit coupled to one or more of motion sensors determines a first position estimate based on one or more of the corresponding signals from the motion sensors. A distance traveled is determined by a motion classifier coupled to one or more of the motion sensors, where the distance estimate is based on one or more of the corresponding motion signals processed in one or more motion models. A Kalman filter is also integrated into the system, where the Kalman filter receives the first position estimate and the distance estimate and provides corrective feedback signals to the inertial processor for the first position estimate. In an additional embodiment, input from a position indicator, such as a GPS, provides a third position estimate, and where the Kalman filter provides corrections to the first position estimate, the distance estimate and parameters of the motion model being used.

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Wayzata, MN, United States of America

Wayne A Soehren