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Recent developments allow for the production of IMU-enabled GPS devices. An IMU allows a GPS receiver to work when GPS-signals are unavailable, such as in tunnels, inside buildings, or when electronic interference is present. [2] IMUs are used in VR headsets and smartphones, and also in motion tracked game controllers like the Wii Remote.
GPS/INS is commonly used on aircraft for navigation purposes. Using GPS/INS allows for smoother position and velocity estimates that can be provided at a sampling rate faster than the GPS receiver. This also allows for accurate estimation of the aircraft attitude (roll, pitch, and yaw) [citation needed] angles.
In 2011, GPS jamming at the civilian level became a governmental concern. [11] The relative ease in ability to jam these systems has motivated the military to reduce navigation dependence on GPS technology. [12] Because inertial navigation sensors do not depend on radio signals unlike GPS, they cannot be jammed. [13]
Today's complex systems use multiple approaches to determine current position. For example, today's most advanced navigation systems are embodied within the Anti-ballistic missile, the RIM-161 Standard Missile 3 leverages GPS, IMU and ground segment data in the boost phase and relative position data for intercept targeting. Complex systems ...
One application of sensor fusion is GPS/INS, where Global Positioning System and inertial navigation system data is fused using various different methods, e.g. the extended Kalman filter. This is useful, for example, in determining the attitude of an aircraft using low-cost sensors. [33]
Invariant extended Kalman filters are for instance used in attitude and heading reference systems. In such systems the orientation, velocity and/or position of a moving rigid body, e.g. an aircraft, are estimated from different embedded sensors, such as inertial sensors, magnetometers, GPS or sonars.
In estimation theory, the extended Kalman filter (EKF) is the nonlinear version of the Kalman filter which linearizes about an estimate of the current mean and covariance.In the case of well defined transition models, the EKF has been considered [1] the de facto standard in the theory of nonlinear state estimation, navigation systems and GPS.
The Kalman filter deals effectively with the uncertainty due to noisy sensor data and, to some extent, with random external factors. The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with ...