Sensor data fusion systems that provide noise reduction and fault protection. The sensor data fusion system fuses data acquired by respective accelerometers having different attributes. For example, one accelerometer has low noise and high bias, while another accelerometer has high noise and low bias when measuring specific force. The high-noise, low-bias accelerometer may be a gravimeter. Gravimeters and traditional accelerometers measure the same physical variable, i.e., specific force. By combining an expensive gravimeter and low-cost accelerometers, a synthetic sensor having both low noise and low bias may be achieved. Such synthetic sensors may be utilized in a gravity anomaly-referenced navigation system to achieve improved navigation performance.

One or more embodiments herein can provide a process to determine dead-reckoning localization of a vehicle. An exemplary system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an obtaining component that obtains plural sensor readings defining movement of a vehicle, wherein the plural sensor readings comprise an inertial sensor reading, a kinematics sensor reading, and an odometry sensor reading, and a generation component that generates, based on the plural sensor readings, a pose value defining a position of the vehicle relative to an environment in which the vehicle is disposed. A sensing sub-system of the exemplary system can comprise an inertial measurement unit sensor, a kinematics sensor, and an odometry sensor.

A system and method for mounting a device to a piece of equipment is presented, and may include a handheld inertial measurement unit (IMU), a computer logic and a mounting interface. The piece of equipment may have a line replaceable unit (LRU) mount that allows a new LRU to be mounted thereon. The mounting interface may be mounted to a LRU mount. The handheld IMU may determine position data with respect to the LRU mount and the piece of equipment. The computer logic may be configured to calculate a positional error value based on the position data to indicate to a user whether corrections need to be made regarding how the new LRU is mounted to the LRU mount before the new LRU is mounted to the LRU mount or whether the new LRU can be mounted to the LRU mount without any corrections.

A calibration scheme measures roll, pitch, and yaw and other speeds and accelerations during a series of vehicle maneuvers. Based on the measurements, the calibration scheme calculates inertial sensor misalignments. The calibration scheme also calculates offsets of the inertial sensors and GPS antennas from a vehicle control point. The calibration scheme can also estimate other calibration parameters, such as minimum vehicle radii and nearest orthogonal orientation. Automated sensor calibration reduces the amount of operator input used when calibrating sensor parameters. Automatic sensor calibration also allows the operator to install an electronic control unit (ECU) in any convenient orientation (roll, pitch and yaw), removing the need for the ECU to be installed in a restrictive orthogonal configuration. The calibration scheme may remove dependencies on a heading filter and steering interfaces by calculating sensor parameters based on raw sensor measurements taken during the vehicle maneuvers.

Systems and methods for off-line and on-line sensor calibration are provided. In certain embodiments, a method for calibrating a sensor comprises receiving at least one reference measurement describing a system state for a system; and receiving at least one sensor measurement from the sensor, wherein the at least one sensor measurement is acquired from an observation of the environment of the system by the sensor. The method also comprises calculating a model residual power spectral density based on the at least one reference measurement and a sensor measurement model; and calculating a measurement residual power spectral density based on the at least one sensor measurement and the at least one reference measurement. Further, the method comprises identifying sensor parameters that morph the model residual power spectral density towards the measurement residual power spectral density.

A movement amount estimation system, comprising a storage area to store acceleration data, for estimating a movement amount of a holder of a mobile terminal, the movement amount estimation system is configured to: detect a start time and an end time of an elevator riding time period of the holder based on the acceleration data; integrate the acceleration data from the start time to the end time to calculate a movement velocity of the holder; correct one of a movement velocity at the start time and a movement velocity at the end time based on another of the movement velocity at the start time and the movement velocity at the end time; and integrate the movement velocity corrected by the time period to estimate a movement amount of the holder when the holder uses an elevator.

A system and method for collecting a set of kinematic data streams from an activity tracking system with at least one activity monitoring system that is attached to a user; segmenting at least a subset of the kinematic data streams into a set of segments according to actions of the user; generating a set of biomechanical signals from the kinematic data streams wherein a biomechanical signal characterize a biomechanical property during a segment and includes generating a first integration data set through data integration and baseline error correction across the set of segments of the normalized kinematic data stream and determining at least a first biomechanical signal that is at least partially based on the first integration data set; and applying the set of biomechanical signals.

A subject tracking system to track a subject is provided. The subject tracking system may include a sensor system, a transmitting unit and a processor. The transmitting unit may be configured to be located with the subject during use and comprising at least one first dedicated high frequency oscillator. The sensor system may include at least one second dedicated high frequency oscillator to continually synchronize the transmitting unit and the sensor system. The processor may continually determine changes in the distance between the subject and the subject tracking system so that a distance between the subject and the autonomous vehicle can be maintained.

A method to improve estimation and stabilization of heading in an inertial navigation system is provided. The method includes operating an inertial measurement unit oriented in a first orientation, forward-rotating the operational inertial measurement unit by a selected-rotation angle about a Z-body axis of the inertial navigation system, wherein the inertial measurement unit is oriented in a second orientation, operating the inertial measurement unit oriented in the second orientation, reverse-rotating the operational inertial measurement unit by the selected-rotation angle about the Z-body axis, wherein the inertial measurement unit is oriented in the first orientation, continuously receiving information indicative of an orientation of the inertial measurement unit at a rotational compensator, and continuously-rotationally compensating navigation module output at the rotational compensator, wherein output of the rotational compensator is independent of the rotating.

Described are techniques and systems for secure camera based IMU calibration for stationary systems, including vehicles. Existing vehicle camera systems are employed, with enhanced security to prevent malicious attempts by hackers to try and cause a vehicle to enter IMU calibration mode. IMU calibration occurs when a calibration system determines the vehicle is parked in a controlled environment; calibration targets are positioned at different viewing angles to vehicle cameras to act as sources of optical patterns of encoded data. Features of the patterns are for security as well as for alignment functionality. Images of the calibration targets enable inference of a vehicle coordinate system, from which calculations for IMU mounting error compensations are performed. A relative rotation between the IMU and the vehicle coordinate system are applied to IMU data to compensate for relative rotations between the vehicle and the IMU, thereby improving vehicle slope and bank metrics.