Therefore, to overcome such a problem, it is a very common practi

Therefore, to overcome such a problem, it is a very common practice to integrate INS with other sensors, which can calibrate the inertial sensor errors. In most outdoor applications, a Kalman filter estimator can be used for optimally combining both IMU and GPS measurements [1]. However, CT99021 an indoor navigation system cannot use GPS since its signals are not available.An alternative approach to calibrate INS errors is via the use of other sensors, such as cameras and magnetic sensors. Combining these two sensors to form a vision-aided inertial navigation system (V-INS) has recently become a popular topic of research [2]. By sensing the Earth’s magnetic field a magnetic sensor can provide a drift-free heading estimate.
Accurate 3-D orientation estimates of a rigid body by inertial/magnetic sensing were exploited in [3], where Inhibitors,Modulators,Libraries the aiding sensors (accelerometer and magnetic sensor) helped mitigate low-frequency gyro drift errors, while, in turn, the signals from the aiding sensors, which are prone to relatively high-frequency errors, are smoothed using gyro data. They are all based on the concept of vector matching, which requires, in principle, the measurements of constant reference vectors (e.g., gravity and the Earth’s magnetic field) [4].In this paper, we present a matrix Kalman filter (MKF) in which the estimate of the state Inhibitors,Modulators,Libraries matrix is expressed in terms of the matrix parameters of the original plant. The MKF has the statistical properties of the ordinary EKF, while retaining the advantages of a compact matrix notation by expressing the estimated matrix in terms of the original plant parameters [5].
The major contribution of this paper is to elucidate under which conditions a MKF-based nonlinear system for indoor Inhibitors,Modulators,Libraries navigation using visual/inertial/magnetic sensors is observable; in other words, the conditions when sufficient information is available for estimating a state matrix that contains, in the present case, the body attitude matrix, the gyro bias vector, Inhibitors,Modulators,Libraries relative velocity vector, the dual part of landmark and the magnetic variation superimposed to the AV-951 magnetic reference vector. For the purpose of orientation determination, an accurately known homogeneous magnetic field in the environment is needed. Magnetic homogeneity is difficult to achieve, especially indoors, due to the presence of iron construction materials in floors, walls and ceilings, or to interferences from various types of equipment.
In order to compensate for magnetic variations, a first-order Gauss-Markov vector random process is chosen to model the magnetic variation. To the best of our knowledge, tech support there has been no such observability analysis so far for the integrated navigation systems in 3-D. We have extended the current work for the observability analysis for an orientation system described in [6], to the 3-D navigation systems based on inertial/visual/magnetic sensors.2.?Sensor Modeling2.1.

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