Abstract: Sensor devices and methods for calculating an orientation of a sensor device including a magnetometer are disclosed. In one embodiment, a method of computing an orientation of a sensor device includes receiving a current magnetic field reading including at least a magnetic field magnitude, calculating a difference between the current magnetic field magnitude and a reference magnetic field magnitude, and comparing the difference between the current magnetic field magnitude and the reference magnetic field magnitude with a threshold. The method further includes adjusting a trust value of the magnetometer such that the trust value is decreased if the difference between the current magnetic field magnitude is greater than or equal to the threshold, and computing an orientation of the sensor device based at least on the trust value and the current magnetic field reading, wherein the trust value affects a reliance on the magnetic field reading in computing the orientation.

Abstract: Sensor devices and methods for producing corrected sensor vectors are disclosed. According to one embodiment, a method of correcting a sensor vector provided by a sensor includes determining a largest magnitude vector component of a sensor vector having N dimensions, and determining a sign of the largest magnitude vector component. The method further includes selecting a major region of interest of a look-up table based on the largest magnitude vector component of the sensor vector and the sign of the largest magnitude vector component, wherein the look-up table includes 2*N major regions of interest, and each major region of interest has a plurality of cells. A cell that is nearest to the sensor vector is selected, and a final corrective data cv is applied to the sensor vector to obtain a corrected sensor vector, wherein the final corrective data cv is associated with the selected cell.

Abstract: Included are embodiments for determining an inertial quantity. One embodiment of a method includes combining readings from a plurality of inertial sensors to produce an estimate of the value of an inertial quantity in a manner that is fault-tolerant, more accurate than traditional sensor arrangements, and able to handle non-linear and non-Gaussian systems. Embodiments of a method also include utilizing a Monte Carlo estimation-based inference system to adaptively combine the inertial sensor outputs into a fault-tolerant highly-accurate inertial quantity estimate, an axis-reversed-paired physical arrangement of inertial sensors to minimize effects of environmental and process noise, and cross-associating sensors to ensure good sensor associations and reduce the effects of sample impoverishment.

Abstract: Sensor devices and methods for calculating an orientation of a sensor device including a magnetometer are disclosed. In one embodiment, a method of computing an orientation of a sensor device includes receiving a current magnetic field reading including at least a magnetic field magnitude, calculating a difference between the current magnetic field magnitude and a reference magnetic field magnitude, and comparing the difference between the current magnetic field magnitude and the reference magnetic field magnitude with a threshold. The method further includes adjusting a trust value of the magnetometer such that the trust value is decreased if the difference between the current magnetic field magnitude is greater than or equal to the threshold, and computing an orientation of the sensor device based at least on the trust value and the current magnetic field reading, wherein the trust value affects a reliance on the magnetic field reading in computing the orientation.

Abstract: Physical sensor devices, methods, and computer useable mediums for estimating an orientation of a physical sensor device are disclosed. According to one embodiment, a method for estimating an orientation of a physical sensor device includes determining a sensed vector associated with a physical sensor and comparing the at least one sensed vector to at least a portion of a plurality of check vectors. Each check vector corresponds to an orientation of the physical sensor device. A reference vector is associated with each check vector, thereby defining a plurality of reference vectors. The method further includes selecting at least one check vector that is closest to the at least one sensed vector, selecting a selected at least one reference vector associated with the selected at least one check vector, and estimating the orientation of the physical sensor device based at least in part on the selected at least one reference vector.

Abstract: Included are embodiments for determining an inertial quantity. One embodiment of a method includes combining readings from a plurality of inertial sensors to produce an estimate of the value of an inertial quantity in a manner that is fault-tolerant, more accurate than traditional sensor arrangements, and able to handle non-linear and non-Gaussian systems. Embodiments of a method also include utilizing a Monte Carlo estimation-based inference system to adaptively combine the inertial sensor outputs into a fault-tolerant highly-accurate inertial quantity estimate, an axis-reversed-paired physical arrangement of inertial sensors to minimize effects of environmental and process noise, and cross-associating sensors to ensure good sensor associations and reduce the effects of sample impoverishment.

Abstract: Included are embodiments for determining an inertial quantity. One embodiment of a method includes combining readings from a plurality of inertial sensors to produce an estimate of the value of an inertial quantity in a manner that is fault-tolerant, more accurate than traditional sensor arrangements, and able to handle non-linear and non-Gaussian systems. Embodiments of a method also include utilizing a Monte Carlo estimation-based inference system to adaptively combine the inertial sensor outputs into a fault-tolerant highly-accurate inertial quantity estimate, an axis-reversed-paired physical arrangement of inertial sensors to minimize effects of environmental and process noise, and cross-associating sensors to ensure good sensor associations and reduce the effects of sample impoverishment.

Abstract: Sensor devices and methods for producing corrected sensor vectors are disclosed. According to one embodiment, a method of correcting a sensor vector provided by a sensor includes determining a largest magnitude vector component of a sensor vector having N dimensions, and determining a sign of the largest magnitude vector component. The method further includes selecting a major region of interest of a look-up table based on the largest magnitude vector component of the sensor vector and the sign of the largest magnitude vector component, wherein the look-up table includes 2*N major regions of interest, and each major region of interest has a plurality of cells. A cell that is nearest to the sensor vector is selected, and a final corrective data cv is applied to the sensor vector to obtain a corrected sensor vector, wherein the final corrective data cv is associated with the selected cell.

Abstract: Physical sensor devices, methods, and computer useable mediums for estimating an orientation of a physical sensor device are disclosed. According to one embodiment, a method for estimating an orientation of a physical sensor device includes determining a sensed vector associated with a physical sensor and comparing the at least one sensed vector to at least a portion of a plurality of check vectors. Each check vector of the plurality of check vectors corresponds to an orientation of the physical sensor device. Additionally, a reference vector is associated with each check vector, thereby defining a plurality of reference vectors. The method further includes selecting at least one check vector that is closest to the at least one sensed vector, selecting a selected at least one reference vector associated with the selected at least one check vector, and estimating the orientation of the physical sensor device based at least in part on the selected at least one reference vector.

Abstract: Included are embodiments for determining an inertial quantity. One embodiment of a method includes combining readings from a plurality of inertial sensors to produce an estimate of the value of an inertial quantity in a manner that is fault-tolerant, more accurate than traditional sensor arrangements, and able to handle non-linear and non-Gaussian systems. Embodiments of a method also include utilizing a Monte Carlo estimation-based inference system to adaptively combine the inertial sensor outputs into a fault-tolerant highly-accurate inertial quantity estimate, an axis-reversed-paired physical arrangement of inertial sensors to minimize effects of environmental and process noise, and cross-associating sensors to ensure good sensor associations and reduce the effects of sample impoverishment.

Abstract: Included are embodiments for determining an inertial quantity. One embodiment of a method includes combining readings from a plurality of inertial sensors to produce an estimate of the value of an inertial quantity in a manner that is fault-tolerant, more accurate than traditional sensor arrangements, and able to handle non-linear and non-Gaussian systems. Embodiments of a method also include utilizing a Monte Carlo estimation-based inference system to adaptively combine the inertial sensor outputs into a fault-tolerant highly-accurate inertial quantity estimate, an axis-reversed-paired physical arrangement of inertial sensors to minimize effects of environmental and process noise, and cross-associating sensors to ensure good sensor associations and reduce the effects of sample impoverishment.