The disclosure discloses a latitude-free initial alignment method under a swaying base based on gradient descent optimization. Firstly, swaying base latitude-free alignment is regarded as a Wahba attitude determination problem to inhibit device noise interference, and an objective function is established based on a gravitational acceleration vector under an earth system; then an exact solution of the objective function is obtained through a gradient descent optimization method, and inertial system conversion quaternion estimation is achieved under the latitude-free condition; and finally, an attitude quaternion is determined by only using information of an accelerometer and a gyroscope of a strapdown attitude heading reference system, and therefore latitude-free initial alignment under the swaying base is achieved. The disclosure can solve the problem that initial alignment cannot be accomplished with unknown latitude under the swaying base, and thus the application range of the strapdown attitude heading reference system is ensured.

Systems and methods for a tightly coupled end-to-end multi-sensor fusion with integrated compensation are described herein. For example, a system includes an inertial measurement unit that produces inertial measurements. Additionally, the system includes additional sensors that produce additional measurements. Further, the system includes one or more memory units. Moreover, the system includes one or more processors configured to receive the inertial measurements and the additional measurements. Additionally, the one or more processors are configured to compensate the inertial measurements with a compensation model stored on the one or more memory units. Also, the one or more processors are configured to fuse the inertial measurements with the additional measurements using a differential filter that applies filter coefficients stored on the one or more memory units. Further, the compensation model and the filter coefficients are stored on the one or more memory units as produced by execution of a machine learning algorithm.

Methods are provided for determining a positioning of a portable device including first and second sensor(s) each having a confidence. These methods include: receiving first and second signals from the first and second sensor(s), respectively; generating positional data representing positional conditions of the portable device and including first and second positional data respectively from the first and second signals, by modelling the received signals based on predefined models defining a correspondence between predefined signals and predefined positional data; comparing the first and second positional data to determine a difference between them; adjusting the confidence of the sensors by determining a new confidence depending on a previous confidence and the determined difference between positional data; weighting the generated positional data depending on corresponding confidences; and determining the positioning of the portable device based on the weighted generated positional data. Computer programs and systems suitable for performing such methods are also provided.

An inertial sensor device includes a first interface, a second sensor, a second interface, a host interface, and a processing circuit. The first interface is an interface for a first sensor configured to detect a first physical quantity in a first detection axis, a second physical quantity in a second detection axis, and a third physical quantity in a third detection axis. The second sensor is configured to detect the physical quantity in the third detection axis as a high-accuracy third physical quantity with a higher accuracy than the first sensor. The processing circuit is configured to output the first physical quantity and the second physical quantity to a host via the host interface, and output the high-accuracy third physical quantity instead of the third physical quantity to the host via the host interface.

The present invention relates to a system (1) configured to select a portion of a virtual scenario (S1v,S2v) during the movement of a vehicle (V) within a geographical area of interest (A). Said system (1) is capable of obtaining information associated with the vehicle (V) regarding the position, the rotation with respect to the x, y, z axes of a reference system associated with the vehicle itself, as well as the heading, and applying this information to a virtual geographical area (Av) associated with said geographical area of interest (A), so that each movement of the vehicle (V) in the geographical area of interest (A) corresponds to a respective movement in the virtual geographical area (Av). In this way, the viewing of a virtual scenario (S1V, S2V) changes according to the movement of the vehicle. The present invention relates also to a vehicle (V) comprising said system, as well as to a method for selecting a portion of a virtual scenario during the movement of a vehicle (V) within a geographical area of interest (A) by means of said system.

A method for stabilizing a magnetic field of an inertial measurement unit (IMU), is provided that includes initializing accelerometer and gyroscope (AG) heading data for the IMU and initializing accelerometer, gyroscope and magnetometer (AGM) heading data for the IMU. Determining whether a tracking state exists and completing processing of the AG heading data and the AGM heading data if the tracking state does not exist. Calculating a magnetic field error if the tracking state exists.

A method and a system for recognizing a two-dimensional (2D) movement track based on a smart watch is provided. The method comprises: acquiring accelerometer signal data and gyroscope signal data of the smart watch; estimating a tilt angle of the smart watch by using the accelerometer signal data and correcting the gyroscope signal data by using the tilt angle; and calculating angle value information of the smart watch by using the corrected gyroscope signal data and estimating a coordinate point. According to the present application, the movement track of the smart watch can be accurately estimated by using the accelerometer and the gyroscope built in the smart watch.

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.

An inertial measurement unit (IMU) self-test system includes an IMU and a control circuit. The control circuit is configured to receive IMU data collected by the IMU and inputs from systems external to the IMU indicative of mechanical stimulus, wherein the control circuit utilizes IMU data collected in response to the mechanical stimulus to determine IMU validity.

A hybrid inertial measurement unit (IMU) comprises: a low frequency (LF) sensor providing a first signal containing information for a first parameter of the hybrid IMU; a shock resistant (SR) sensor providing a second signal containing information for the first parameter, wherein the SR sensor is resistant to destabilization during a destabilizing operational period; and a processor, wherein the processor further comprises: a weighting factor computation module to compute a weight to be applied to the first signal and to compute a weight to be applied to the second signal; a LF weighting module to apply the computed weight to the first signal to create a weighted first signal; a SR weighting module to apply the computed weight to the second signal to create a weighted second signal; and a compensator to combine the weighted first signal and the weighted second signal to create a compensated signal containing information for the first parameter.