The disclosure relates to a method for operating an inertial sensor of a vehicle, in particular a motor vehicle, wherein measurement data of at least one measurement variable of the inertial sensor are captured during operation of the vehicle and are checked for error values in order to calibrate the inertial sensor. According to the disclosure, during operation of the vehicle, measurement data of a different measurement variable, which, however, correlates with the measurement variable of the inertial sensor, are captured by means of a reference sensor and are compared with the measurement data of the inertial sensor in order to record the error values in accordance with a deviation of the measurement data of the inertial sensor from the measurement data of the reference sensor.

An exercise analysis device includes: an exercise analysis unit that generates exercise information during running or walking of a subject using output of an inertial measurement unit (IMU); and an output unit that converts exercise information periodically generated among the exercise information into predetermined perceptual information and outputs the perceptual information in synchronization with landing.

Techniques are disclosed for systems and methods for navigating mobile structures. The mobile structure may include a main attitude & heading reference system (AHRS) and one or more devices. The one or more devices may include a slave AHRS such as a gyroscope. Data may be transmitted from the main AHRS to the one or more devices through a network. Latency may be present in the transmission of data. As such, data from the slave AHRS may be used to determine changes in heading and/or attitude of the mobile structure to compensate for such latency. In addition, such data may be used to determine changes in wind direction and/or heading experienced by the mobile structure.

A sensor module includes an X-axis angular velocity sensor device that outputs digital X-axis angular velocity data, a Y-axis angular velocity sensor device that outputs digital Y-axis angular velocity data, a Z-axis angular velocity sensor device that outputs digital Z-axis angular velocity data, an acceleration sensor device that outputs digital X-axis, Y-axis, and Z-axis acceleration data, a microcontroller, a first digital interface bus that electrically connects the X-axis angular velocity sensor device, the Y-axis angular velocity sensor device, and the Z-axis angular velocity sensor device to a first digital interface, and a second digital interface bus that electrically connects the acceleration sensor device to a second digital interface.

Various embodiments include methods for performing temperature calibration of a first temperature sensitive unit with an electronic device having a first processing unit that is thermally coupled to the first temperature sensitive unit. Various embodiments may include determining a current temperature of the first temperature sensitive unit, determining a processing load for the first processing unit based on the current temperature and a target temperature, applying the determined processing load to the first processing unit to vary a temperature of the first temperature sensitive unit, and determining a temperature bias for the first temperature sensitive unit at the temperature of the first temperature sensitive unit based on an output of the first temperature sensitive unit.

Techniques provided herein are directed toward resolving a direction of travel of a mobile device based on MEMS sensor data by identifying a type of motion the mobile device is subject to and offsetting vertical acceleration data with horizontal acceleration data by a predetermined time offset based on the identified type of motion. The offset vertical and horizontal acceleration data can then be used to resolve, with an increased accuracy, a direction of travel of the mobile device.

Described is a system for estimating a trajectory of a borehole. The system processes signals of sensor streams obtained from an inertial sensor system. Using the set of processed signals, the system determines whether a drill is in a survey mode state or a continuous mode state, and a measured depth of the borehole is determined. A set of survey mode positioning algorithms is applied when the drill is stationary. A set of continuous mode navigation algorithms is applied when the drill is non-stationary. Using at least one Kalman filter, results of the set of survey mode positioning algorithms and the set of continuous mode navigation algorithms are combined. An estimate of a borehole trajectory and corresponding ellipse of uncertainty (EOU) is generated using the combined results.

Systems and methods for predicting aircraft navigation performance are provided. In one embodiment, a method can include determining that one or more navigational aid measurements are not available to the aircraft. The method can include estimating a future actual navigation performance of the aircraft for a future point in the flight plan. The method can include determining a future required navigation performance associated with the future point in the flight plan. The method can include comparing the future actual navigation performance to the future required navigation performance to determine if the future actual navigation performance satisfies the future required navigation performance. The method can include providing, to an onboard system of the aircraft, information indicative of whether the future actual navigation performance satisfies the future required navigation performance.

An ensemble of motion sensors is tested under known conditions to automatically ascertain instrument biases, which are modeled as autoregressive-moving-average (ARMA) processes in order to construct a Kalman filter. The calibration includes motion profiles, temperature profiles and vibration profiles that are operationally significant, i.e., designed by means of covariance analysis or other means to maximize, or at least improve, the observability of the calibration model's structure and coefficients relevant to the prospective application of each sensor.

Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.