Sensor fusion in the context of Sensor

A virtual reality headset (VR headset) is a head-mounted device that uses 3D near-eye displays and positional tracking to provide a virtual reality environment for the user. VR headsets are widely used with VR video games, but they are also used in other applications, including simulators and trainers. VR headsets typically include a stereoscopic display (providing separate images for each eye), stereo sound, and sensors like accelerometers and gyroscopes for tracking the pose of the user's head to match the orientation of the virtual camera with the user's eye positions in the real world. Mixed reality (MR) headsets are VR headsets that enable the user to see and interact with the outside world. Examples of MR headsets include the Apple Vision Pro and Meta Quest 3.

VR headsets typically use at least one MEMS IMU for three degrees of freedom (3DOF) motion tracking, and optionally more tracking technology for six degrees of freedom (6DOF) motion tracking. 6DOF devices typically use a sensor fusion algorithm to merge the data from the IMU and any other tracking sources, typically either one or more external sensors, or "inside-out" tracking using outward facing cameras embedded in the headset. The sensor fusion algorithms that are used are often variants of a Kalman filter. VR headsets can support motion controllers, which similarly combine inputs from accelerometers and gyroscopes with the headset's motion tracking system.

Soft sensor or virtual sensor is a common name for software where several measurements are processed together. Commonly soft sensors are based on control theory and also receive the name of state observer. There may be dozens or even hundreds of measurements. The interaction of the signals can be used for calculating new quantities that need not be measured. Soft sensors are especially useful in data fusion, where measurements of different characteristics and dynamics are combined. It can be used for fault diagnosis as well as control applications.

Well-known software algorithms that can be seen as soft sensors include Kalman filters. More recent implementations of soft sensors use neural networks or fuzzy computing.

An attitude and heading reference system (AHRS) consists of sensors on three axes that provide attitude information for aircraft, including roll, pitch, and yaw. These are sometimes referred to as MARG (Magnetic, Angular Rate, and Gravity) sensors and consist of either solid-state or microelectromechanical systems (MEMS) gyroscopes, accelerometers and magnetometers. They are designed to replace traditional mechanical gyroscopic flight instruments.

The main difference between an Inertial measurement unit (IMU) and an AHRS is the addition of an on-board processing system in an AHRS, which provides attitude and heading information. This is in contrast to an IMU, which delivers sensor data to an additional device that computes attitude and heading. With sensor fusion, drift from the gyroscopes integration is compensated for by reference vectors, namely gravity, and the Earth's magnetic field. This results in a drift-free orientation, making an AHRS a more cost effective solution than conventional high-grade IMUs that only integrate gyroscopes and rely on a high bias stability of the gyroscopes.In addition to attitude determination an AHRS may also form part of an inertial navigation system.