This disclosure relates to an underground mining vehicle comprising a three-axis MEMS gyroscope rotatable about a rotation axis and a gyroscope interface that calculates a first rotation rate bias with respect to a first axis different to the rotation axis, a second rotation rate bias with respect to a second axis different to the first axis and different to the rotation axis, a rotation rate about the rotation axis based on the Earth rotation rate vector by correcting the rotational measurement data using the first rotation rate bias and the second rotation rate bias and a third rotation rate bias with respect to the rotation axis based on the calculated rotation rate about the rotation axis. A navigation unit receives the first rotation rate bias, the second rotation rate bias and the third rotation rate bias and calculates a pose of the vehicle.

Techniques are provided for determination of a guided-munition orientation during flight based on lateral acceleration, velocity, and turn rate of the guided-munition. A methodology implementing the techniques, according to an embodiment, includes obtaining a lateral acceleration vector measurement and a velocity of the guided-munition, and calculating a ratio of the two, to generate an estimated lateral turn vector of the guided-munition. The method also includes integrating the estimated lateral turn vector, over a period of time associated with flight of the guided-munition, to generate a first type of predicted attitude change. The method further includes obtaining and integrating a lateral turn rate vector measurement of the guided-munition, over the period of time associated with flight of the guided-munition, to generate a second type of predicted attitude change. The method further includes calculating a gravity direction vector based on a difference between the first and second types of predicted attitude change.

A computer-implemented method of determining a location of a mobile device is provided. The method can include receiving inertial data generated at the mobile device, the inertial data including a plurality of samples taken at different times, segmenting the inertial data into pseudo-independent windows, wherein each pseudo-independent window can include a plurality of the samples and wherein one or more initial states for each pseudo-independent window are treated as unknown, estimating a change in navigation state over each pseudo-independent window using the samples of inertial data, and summing the changes in the navigation states over the pseudo-independent windows so as to determine the location of the mobile device.

A method of calibrating an inertial measurement unit, the method comprising: (a) collecting data from the inertial measurement unit while stationary as a first step; (b) collecting data from the inertial measurement unit while repositioning the inertial measurement unit around three orthogonal axes of the inertial measurement unit as a second step; (c) calibrating a plurality of gyroscopes using the data collected during the first step and the second step; (d) calibrating a plurality of magnetometers using the data collected during the first step and the second step; (e) calibrating a plurality of accelerometers using the data collected during the first step and the second step; (f) where calibrating the plurality of magnetometers includes extracting parameters for distortion detection and using the extracted parameters to determine if magnetic distortion is present within a local field of the inertial measurement unit.

A system (10) for pedestrian use includes an accelerometer (110) having multiple electronic sensors; an electronic circuit (100) operable to generate a signal stream representing magnitude of overall acceleration sensed by the accelerometer (110), and to electronically correlate a sliding window (520) of the signal stream with itself to produce peaks at least some of which represent walking steps, and further operable to electronically execute a periodicity check (540) to compare different step periods for similarity, and if sufficiently similar then to update (560) a portion of the circuit substantially representing a walking-step count; and an electronic display (190) responsive to the electronic circuit (100) to display information at least in part based on the step count. Other systems, electronic circuits and processes are disclosed.

A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional “single epoch” methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.

A method of calibrating an inertial measurement unit, the method comprising: (a) collecting data from the inertial measurement unit while stationary as a first step; (b) collecting data from the inertial measurement unit while repositioning the inertial measurement unit around three orthogonal axes of the inertial measurement unit as a second step; (c) calibrating a plurality of gyroscopes using the data collected during the first step and the second step; (d) calibrating a plurality of magnetometers using the data collected during the first step and the second step; (e) calibrating a plurality of accelerometers using the data collected during the first step and the second step; (f) where calibrating the plurality of magnetometers includes extracting parameters for distortion detection and using the extracted parameters to determine if magnetic distortion is present within a local field of the inertial measurement unit.

An attitude sensor system with automatic bias correction having a primary attitude sensor wherein the primary attitude sensor comprises at least one accelerometer and an auxiliary sensor system configured to automatically estimate a bias of the accelerometer of the primary attitude sensor such that the resulting error is removed from an output of the attitude sensor system.

A distance notification device includes a first and a second positional information acquiring units that acquire positional information on a user and peripheral users respectively at a predetermined time interval; a distance calculating unit that calculates respective distances between the user and the peripheral users based on the positional information; a distance determining unit that determines whether each of the distances is equal to or longer than a predetermined distance; a coordinate information calculating unit that calculates and update coordinate information on a meeting point based on the positional information; and a display that displays various images; wherein the display further notifies, by updating sequentially, the user of directional information toward the meeting point when a number of the peripheral users at a distance equal to or longer than the predetermined distance from the user is equal to or larger than a predetermined number.

A method and system for combining data obtained by sensors, having particular application in the field of navigation systems, are disclosed. The techniques provide significant improvement over state-of-the-art Markovian methods that use statistical noise filters such as Kalman filters to filter data by comparing instantaneous data with the corresponding instantaneous estimates from a model. In contrast, the techniques disclosed herein use multiple time periods of various lengths to process multiple sensor data streams, in order to combine sensor measurements with motion models at a given time epoch with greater confidence and accuracy than is possible with traditional “single epoch” methods. The techniques provide particular benefit when the first and/or second sensors are low-cost sensors (for example as seen in smart phones) which are typically of low quality and have large inherent biases.