Methods, architectures, apparatuses, systems, devices, and computer program products directed to device position tracking are provided. Device position tracking may rely on maintaining frame alignment between tracking-system and tracked-device frames. Performing frame alignment may include determining an alignment transformation that may (e.g., best) align a linear position measured at a tracking system with a linear acceleration measured at a tracked device. The alignment transformation may be applied to align the linear position and any signal in the device frame, such as any of angular velocity, angular acceleration, angular position, gravity, linear velocity, linear position or magnetometer in the device frame. Once aligned, the linear position and such signal in the device frame may be combined.

Disclosed is a self-adaptive horizontal attitude measurement method based on motion state monitoring. Based on a newly established state space model, a horizontal attitude angle is taken as a state variable, an angular velocity increment ??.sup.b for compensating a random constant zero offset is taken as a control input of a system equation, and a specific force f.sup.b for compensating the random constant zero offset is taken as a measurement quantity. Meanwhile, judgment conditions for a maneuvering state of a carrier are improved, and maneuvering information of the carrier is judged by comprehensively utilizing acceleration information and angular velocity information output by a micro electro mechanical system inertial measurement unit (MEMS-IMU), whereby a measurement noise matrix of a filter can be automatically adjusted, thereby effectively reducing the influence of carrier maneuvering on the calculation of a horizontal attitude. The method has no special requirement on the maneuvering state of the carrier, and can ensure that the system has high attitude measurement precision in different motion states without an external information assistance.

An inertial measurement unit (IMU) includes an inertial sensor assembly including a plurality of accelerometers and a plurality of rate gyroscopes, an inertial sensor compensation and correction module, and a Kalman estimator module. The inertial sensor compensation and correction module is configured to apply a set of error compensation values to sensed acceleration and rotational rate to produce a compensated acceleration and a compensated rotational rate of the IMU. The Kalman estimator module is configured to determine a set of error correction values based on a difference between a change in integrated acceleration of the IMU and a change in true airspeed of the IMU. The inertial sensor compensation and correction module is further configured to apply the set of error correction values to each of the compensated acceleration and the compensated rotational rate to output an error-corrected acceleration and an error-corrected rotation rate.

Propulsion of an unmanned vehicle may include determining and ordering a subset of altitude-differentiated wind vectors, the subset facilitating directional air flow from a starting geographic region to a destination geographic region, and configuring the vehicle and adjusting the altitude of the vehicle to the altitude corresponding to each of the subset of wind vectors as ordered based on a flight plan that includes at least one of a duration and distance for each of the ordered subset of the wind vectors.

An approach is provided by a mobile information handling system that includes a processor, and a gyroscope, a gravity sensor, and a memory each accessible by the processor. The approach identifies, at the mobile device that is moving with a vehicle, when a rotation of the gyroscope is at a near-zero moment, and an acceleration, wherein the acceleration is detected as being on a plane that is near-perpendicular with a gravity sensed by the gravity sensor. Then the detection is made, the approach determines a direction of the vehicle as being parallel to a direction of the detected acceleration. The approach then aligns a coordinate system used in the mobile device based on the determined direction of the vehicle.

Methods and systems are provided that enable accurate driving behavior data (e.g., vehicle acceleration data) to be obtained by a mobile device, despite the reference frames of the mobile device and the vehicle occasionally moving relative to each other. Accordingly, a user does not have to maintain a mobile device stationary relative to a vehicle in order to have a high likelihood that accurate driving data is collected.

An object pose measurement system based on MEMS IMU is disclosed, comprising: an accelerometer, a magnetometer, a gyroscope, an object vector information calculation unit, and a rotation compensation unit; wherein the object vector information calculation unit connected respectively to the accelerometer, magnetometer, gyroscope to receive respective measurement data and calculating at least an object vector information; the rotation compensation unit connected to the object vector information calculation unit to receive the at least an object vector information, compute and output a rotated compensated object vector information; wherein the rotation compensation unit performing a quaternion rotation compensation computation and outputting the rotated compensated quaternion as a rotated compensated object vector information.

Systems and methods for determining pose parameters of an inertial measurement unit (IMU) sensor include collecting measurement data generated by IMU sensors, using a processor to temporally integrate the measurement data, including any errors, generating a temporally continuous error propagation model, and temporally integrating the model to generate one or more compensation gradients for said pose parameters.

To accurately calculate an attached angle of an accelerometer and accurately correct an acceleration that is obtained from the accelerometer: a frequency analyzing module of an acceleration corrector divides a sensor coordinate system acceleration into a bias frequency component, a gravity frequency component, a movement acceleration frequency component, and a noise frequency component, through a wavelet transform. The frequency analyzing module outputs a sum component of the gravity frequency component and the movement acceleration frequency component to an attached angle estimating module and a correcting operation module. The attached angle estimating module estimation-calculates the attached angle of an accelerometer and outputs it to the correcting operation module. The correcting operation module corrects the acceleration, which is including of the sum component of the gravity frequency component and the movement acceleration frequency component, based on the estimation-calculated attached angle to calculate a movable body coordinate system acceleration.

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.