Examples of systems and methods for locating movable objects such as carts (e.g., shopping carts) are disclosed. Such systems and methods can use dead reckoning techniques to estimate the current position of the movable object. Various techniques for improving accuracy of position estimates are disclosed, including compensation for various error sources involving the use of magnetometer and accelerometer, and using vibration analysis to derive wheel rotation rates. Various techniques utilize characteristics of the operating environment in conjunction with or in lieu of dead reckoning techniques, including characteristic of environment such as ground texture, availability of signals from radio frequency (RF) transmitters including precision fix sources. Navigation techniques can include navigation history and backtracking, motion direction detection for dual swivel casters, use of gyroscopes, determining cart weight, multi-level navigation, multi-level magnetic measurements, use of lighting signatures, use of multiple navigation systems, or hard/soft iron compensation for different cart configurations.

A method of determining whether parametric performance of an inertial sensor has been degraded comprises: recording first data output from an inertial sensor; then recording second data output from the inertial sensor; comparing the first data output with the second data output; and determining whether the parametric performance of the inertial sensor has been degraded based on the comparison between the first and second data output.

An attitude and heading reference system (AHRS) for a vehicle comprises an IMU that generates inertial measurements, a heading source that generates heading measurements, an attitude filter in communication with the IMU, and a heading filter in communication with the heading source. The attitude filter receives inertial measurements from the IMU; computes an attitude estimation comprising attitude estimates and covariances for the attitude estimates; and outputs the attitude estimation. The heading filter receives the heading measurements, when available, from the heading source; receives the attitude estimation, comprising the attitude estimates and the covariances for the attitude estimates; computes a heading estimation comprising a heading mean estimate and a heading variance; and outputs the heading estimation. The attitude estimation output from the attitude filter is independent of any availability of the heading measurements, such that the attitude estimation output is available at all earth regions during operation of the vehicle.

An apparatus for measuring gravity acceleration of a drilling tool comprises sensors and a measurement circuit. The sensor comprises a three-axis gravity accelerometer, a reference measurement sensor and a temperature sensor. The three-axis gravity accelerometer measures acceleration component signals in three mutually orthogonal directions, and the reference measurement sensor generates a signal that varies with rotation and is not affected by vibration or shock to serve as a reference signal. The temperature sensor measures the temperature in the apparatus to compensate the temperature effect of the gravity accelerometers. The measurement circuit acquires output signals of the sensors and performs cross-correlation processing on the accelerometer components using the reference signal to extract gravity acceleration signals so as to eliminate centrifugal acceleration, vibration, shock and other interferences generated by rotation. The non-interference gravity acceleration signals is used for calculating an inclination angle and a toolface angle of a drilling tool in the rotating state.

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.

A method includes receiving at least one wireless signal measurement and motion sensor measurements. The method also includes generating a location estimate based on the at least one wireless signal measurement. The method also includes determining whether a step is present based on the motion sensor measurements. The method also includes, in response to determining that a step is present, determining a step heading offset based on the location estimate and the motion sensor measurements, and determining a step length and heading based on the motion sensor measurements and the step heading offset. The method also includes determining a location of an object based on at least one of (i) the at least one wireless signal measurement or (ii) the step length and heading.

Disclosed are a correlation coefficient correction method, a correlation coefficient correction apparatus, and a program, capable of improving estimation accuracy of a walking velocity or a stride of a moving object, and an exercise analysis method capable of analyzing a user's exercise with high accuracy. In one aspect, the correlation coefficient correction method includes calculating a reference velocity by using a detection result in a first sensor, calculating characteristic information regarding walking of a moving object by using a detection result in a second sensor mounted on the moving object, and correcting a correlation coefficient in a correlation expression indicating a correlation between the characteristic information and a walking velocity or a stride of the moving object by using the reference velocity.

The invention relates to a navigational aid method for an inertial navigation system including at least one inertial sensor (4) having a sensitive axis (X-X), each inertial sensor (4) comprising an ASG gyroscope (8) able to deliver an ASG signal representative of a rotation about the corresponding sensitive axis (X-X), and a MEMS gyroscope (10) able to deliver a MEMS signal representative of a rotation about the corresponding sensitive axis (X-X), the method including the steps of: between a first date and a subsequent third date, calculating a path from the MEMS signals; from the third date, calculating the path from the ASG signals; estimating a bias vector introduced by the MEMS gyroscopes (10), from the MEMS signals and ASG signals; at a fourth date subsequent to the third date, resetting the path.

In an inertial sensor that includes an angular rate detection circuit having a structure synchronized with a resonant frequency of an angular rate detection element, an object thereof is to realize an angle output having high accuracy with less integration error in an integration circuit for detecting an angle. The inertial sensor includes an angular rate detection element chip C1 that has a mechanical structure for angular rate detection; and a signal processing LSI chip C2 that is angular rate detection circuit for detecting an angular rate from the angular rate detection element chip C1. The signal processing LSI chip C2 calculates an angle by sampling a signal obtained from the angular rate detection element chip C1 at a discrete time synchronized with a drive frequency of the angular rate detection element chip C1.

Methods and systems are provided herein for measuring 3D apparatus (e.g., manual tool or tool accessory) orientation. Example implementations use an auto-nulling algorithm that incorporates a quaternion-based unscented Kalman filter. Example implementations use a miniature inertial measurement unit endowed with a tri-axis gyro and a tri-axis accelerometer. The auto-nulling algorithm serves as an in-line calibration procedure to compensate for the gyro drift, which has been verified to significantly improve the estimation accuracy in three-dimensions, especially in the heading estimation.