LORD 6254-4220, User Manual

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3DM
®
-GX5-15
User Manual
5.5 Angular Rate and Acceleration Limits
The 3DM-GX5-15 angular rate and acceleration ranges depend on the sensors installed in the device.
Exceeding the specified range for either sensor will result in estimated state errors and elevated
uncertainties until the over-range event is corrected and the filter can resolve the errors.
5.6 Communications Bandwidth
When selecting sensor and estimation outputs to be recorded, communication bandwidth
considerations should be taken into account, especially when using RS232 serial communications.
Lower baud rates equate to lower communication bandwidth, which can be consumed quickly by
selecting a large number of measurements at high sample rates. Overrunning the communication
bandwidth will result in dropped data packets. Most computer RS232 ports are limited to 115,200 baud
even though the 3DM-GX5-15 is capable of running at 921,600 baud.
5.7 Estimation Filter Operation
The 3DM-GX5-15 combines the information from the IMU sensors to calculate an orientation solution
that incorporates the strengths of the individual while minimizing their weaknesses.
The 3DM-GX5-15 runs an Auto-Adaptive Extended Kalman Filter as a full-state dynamics model. The
state propagation utilizes Newton’s and Euler’s equations of motion with the acceleration and angular
rate treated as control inputs.
The Kalman filter estimates the full state of attitude for a total of 16 states: 4 attitude (quaternion), 3
accel bias, 3 gyro bias, 3 accel scale factor, and 3 gyro scale factor states. The gyro bias states are
estimated to compensate for the time-varying biases inherent in MEMS gyros, which are the largest
error sources for these devices.
The Kalman filter also provides statistical information about the quality of the estimated states
described in a covariance matrix. The diagonal terms of the matrix are the variance of each state, thus
the square root of these values are 1- sigma standard deviations. These values give the filter’s
estimation of how well it knows the individual states, given what it knows about the statistical properties
of the noise sources of the various sensors and also provide feedback to the user as uncertainty values.
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