This function is used to deduce the calibration constants for a triaxial field sensor, such as an accelerometer or magnetometer, based on movement data. This can be used to do a 'bench' calibration of a sensor.

## Arguments

- X
The segment of triaxial sensor data to calibrate. It must be a 3-column matrix. X can come from any triaxial field sensor and can be in any unit and any frame.

- n
The target field magnitude e.g., 9.81 for accelerometer data using m/s^2 as the unit.

- method
An optional string selecting the type of calibration. The default is to calibrate for offset and scaling only. Other options are: 'gain' adjust gain of axes 2 and 3 relative to 1, or 'cross' adjust gain and remove cross-axis correlations

## Value

A list with 2 elements:

**Y:**The matrix of converted sensor values. These will have the same units as for input argument n. The size of Y is the same as the size of X and it has the same frame and sampling rate.**G:**The calibration structure containing fields: G.poly is a matrix of polynomials. The first column of G.poly is the three scale factors applied to the columns of X. The second column is the offset added to each column of X after scaling. G.cross is a 3x3 matrix of cross-factors. If there are no cross-terms, this is the identity matrix. Off-axis terms correct for cross-axis sensitivity.

A message will also be printed to the screen presenting

## Details

The function reports the residual and the axial balance of the data. A low residual e.g., <5% indicates that the data can be calibrated well and there is not much noise. The axial balance indicates whether the movement in X is suitable for data-driven calibration. If the movement covers all directions fairly equally, the axial balance will be high. A balance <20 % may lead to unreliable calibration. For bench calibrations, a high axial balance is achieved by rotating the sensor through the full 3-dimensions. Sampling rate and frame of Y are the same as the input data so Y has the same size as X. The units of Y are the same as the units used for n. If n is not specified, the units of Y are the same as for the input data. It is a good idea to low-pass filter and/or remove outliers from the sensor data before using this function to reduce errors from specific acceleration and sensor noise.