Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: A method for determining an environmental pressure change affecting a pressure sensor within a portable device to determine an elevation of the portable device is disclosed. The method involves estimating a location of the mobile device, estimating an atmospheric pressure associated with the mobile device at a server based on data indicative of atmospheric pressure received from the mobile device, and generating the elevation of the mobile device based on the atmospheric pressure associated with the mobile device and reference data indicative of an absolute elevation reference. The absolute elevation determined may be based on the estimated location of the mobile device and elevation data obtained from a reference map.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: Methods for calibrating a body-worn magnetic sensor by spinning the magnetic sensor 360 degrees to capture magnetic data; if the spin failed to produce a circle contained in an x-y plane fit a sphere to the captured data; determining offsets based on the center of the sphere; and removing the offsets that are in the z-direction. Computing a magnetic heading reliability of a magnetic sensor by determining an orientation of the sensor at one location; transforming the orientation between two reference frames; measuring a first vector associated with the magnetic field of Earth at the location; processing the first vector to generate a virtual vector when a second location is detected; measuring a second vector associated with the magnetic field of Earth at the second location; and calculating the magnetic heading reliability at the second location based on a comparison of the virtual vector and the second vector.

Abstract: Methods and systems are described for determining the elevation of tracked personnel or assets (trackees) that can take input from mounted sensors on each trackee (including barometric, inertial, magnetometer, radio frequency ranging and signal strength, light and GPS sensors), external constraints (including ranging constraints, feature constraints, and user corrections), and terrain elevation data. An example implementation of this method for determining elevation of persons on foot is described. But this method is not limited to computing elevation of personnel or to on foot movements.

Abstract: A system and method for recognizing features for location correction in Simultaneous Localization And Mapping operations, thus facilitating longer duration navigation, is provided. The system may detect features from magnetic, inertial, GPS, light sensors, and/or other sensors that can be associated with a location and recognized when revisited. Feature detection may be implemented on a generally portable tracking system, which may facilitate the use of higher sample rate data for more precise localization of features, improved tracking when network communications are unavailable, and improved ability of the tracking system to act as a smart standalone positioning system to provide rich input to higher level navigation algorithms/systems. The system may detect a transition from structured (such as indoors, in caves, etc.) to unstructured (such as outdoor) environments and from pedestrian motion to travel in a vehicle.

Abstract: A system and method for locating, tracking, and/or monitoring the status of personnel and/or assets (collectively “trackees”), both indoors and outdoors, is provided. Tracking data obtained from any number of sources utilizing any number of tracking methods (e.g., inertial navigation and signal-based methods) may be provided as input to a mapping application. The mapping application may generate position estimates for trackees using a suite of mapping tools to make corrections to the tracking data. The mapping application may further use information from building data, when available, to enhance position estimates. Indoor tracking methods including, for example, sensor fusion methods, map matching methods, and map building methods may be implemented to take tracking data from one or more trackees and compute a more accurate tracking estimate for each trackee.

Abstract: A system and method for locating, tracking, and/or monitoring the status of personnel and/or assets (collectively “trackees”), both indoors and outdoors, is provided. Tracking data obtained from any number of sources utilizing any number of tracking methods (e.g., inertial navigation and signal-based methods) may be provided as input to a mapping application. The mapping application may generate position estimates for trackees using a suite of mapping tools to make corrections to the tracking data. The mapping application may further use information from building data, when available, to enhance position estimates. Indoor tracking methods including, for example, sensor fusion methods, map matching methods, and map building methods may be implemented to take tracking data from one or more trackees and compute a more accurate tracking estimate for each trackee.