A method for filtering the signals arising from a sensor assembly (EC) comprising at least one measurement sensor for measuring a vector physical field which is substantially constant over time and in space in a reference frame, said sensor assembly (EC) being tied in motion to a moving frame, moving in the reference frame, the method comprising the steps consisting in: applying a first transformation (T1) to the measurements of a measurement sensor of the sensor assembly (EC) which are provided in the moving frame, to a pseudo reference frame, with the aid of a first change-of-frame operator (R(t)) by rotation between the moving frame and the pseudo reference frame; and applying a filtering (FILT) to the measurements thus transformed in the pseudo reference frame; and applying a second transformation (T2), the inverse of said first transformation, to the measurements filtered by said filtering (FILT), from the reference frame to the moving frame, with the aid of a second change-of-frame operator (R.sup.1(t)) by rotation between the pseudo reference frame and the moving frame, the inverse of said first operator (R(t)).

A device configured to determine a rotation angle with respect to a proceeding direction includes an accelerometer configured to obtain acceleration information related to movements of the device; a storage unit configured to store the acceleration information; and a controller configured to transform coordinates of the acceleration information, filter the acceleration information on the transformed coordinates, and determine the rotation angle of the device with respect to the proceeding direction of the device by using gravitational acceleration information on the transformed coordinates. The gravitational acceleration information is derived by using information about a time point when a vertical acceleration component at the transformed coordinates of the device has a maximum value, from among the acceleration information.

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.

An object pose measurement system based on MEMS IMU includes: an accelerometer, a magnetometer, a gyroscope, an object vector information calculation unit, and a rotation compensation unit; wherein the object vector information calculation unit is connected respectively to the accelerometer, magnetometer, gyroscope to receive respective measurement data and calculate object vector information; the rotation compensation unit is connected to the object vector information calculation unit to receive the object vector information, compute and output rotation compensated object vector information; wherein the rotation compensation unit performs quaternion rotation compensation computation and outputs the rotation compensated quaternion as the rotation compensated object vector information.

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.

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.

An inertial measurement system (200) for a longitudinal projectile, comprising a first, roll gyro to be oriented substantially parallel to the longitudinal axis of the projectile; a second gyro and a third gyro with axes arranged with respect to the roll gyro such that they define a three dimensional coordinate system. The system further comprises a controller (225, 250), arranged: to compute a current projectile attitude from the outputs of the first, second and third gyros, the computed attitude comprising a roll angle, a pitch angle and a yaw angle; for at least two time points, to compare the computed pitch and yaw angles with expected values for the pitch and yaw angles; for each of said at least two time points, to calculate a roll angle error based on the difference between the computed pitch and yaw angles and the expected pitch and yaw angles; to calculate a roll angle error difference between said at least two time points; to calculate the total roll angle subtended between said at least two time points; to calculate a roll angle scale factor error based on said computed roll angle error difference and said total subtended roll angle and apply the calculated roll angle scale factor error to the output of the roll gyro.

A system comprises a plurality of sensors, a sensor processor, and a sampling rate engine. The sensor processor is coupled to an output of each sensor of the plurality of sensors. The sensor processor estimates user dynamics in response to a first output signal of a first sensor of the plurality of sensors. The sampling rate engine is coupled to an output of the sensor processor. The sampling rate engine determines a sampling rate value of a second sensor of the plurality of sensors in response to a user dynamics value from the sensor processor. The second sensor comprises a selectable sampling rate. The selectable sampling rate is configured in response to the sampling rate value determined by the sampling rate engine.

A mobile device and a method for estimation of direction of motion of a user are described. The mobile device comprises an inertial sensor to capture acceleration signals based on motion of the user and a direction estimation module. The direction estimation module determines direction of gravity based on filtering acceleration values obtained from captured the acceleration signals using a low-pass filter to identify a plane orthogonal to the direction of gravity. The plane orthogonal to the gravity comprises two orthogonal axes orthogonal to the direction of gravity. Further, displacement values are evaluated based on a user input for placement of the mobile device with respect to user's body, and integration of the acceleration values across the two orthogonal axes with respect to time. A direction of motion of the user is estimated based on a ratio of the displacement values along the two orthogonal axes.

An information processing device includes at least one processor. The processor acquires statistic information corresponding to a position or area obtained by an activity involving movement, acquires a feature quantity of the position or area obtained based on the statistic information, and associates description information corresponding to the feature quantity with a representative point that is a point representing the position or area.