Motion Correction

The Nortek Vector ADV can be purchased with an Inertial Motion Unit (IMU) that measures the ADV motion. These measurements can be used to remove motion from ADV velocity measurements when the ADV is mounted on a moving platform (e.g. a mooring). This approach has been found to be effective for removing high-frequency motion from ADV measurements, but cannot remove low-frequency (\(\lesssim\) 0.03Hz) motion because of bias-drift inherent in IMU accelerometer sensors that contaminates motion estimates at those frequencies.

This documentation is designed to document the methods for performing motion correction of ADV-IMU measurements. The accuracy and applicability of these measurements is beyond the scope of this documentation (journal articles are forthcoming).

Nortek’s Signature ADP’s are now also available with an IMU, but DOLfYN does not yet support motion correction of ADP data. I hope to remedy this soon.

Pre-Deployment Requirements

In order to perform motion correction the ADV-IMU must be assembled and configured correctly:

1. The ADV head must be rigidly connected to the ADV body (pressure case, which contains the IMU sensor).

   1. For fixed-head (fixed-stem) ADVs the body is rigidly connected to the body.
   2. For cable-head (cabled-probe) ADVs, a support structure must be constructed that rigidly connects the ADV body to the head.

2. The ADV software must be configured properly. In the ‘Deployment Planning’ frame of the Vector Nortek Software, be sure that:

   1. The IMU sensor is enabled (checkbox) and set to record ‘dAng dVel Orient’.
   2. The ‘Coordinate system’ must be set to ‘XYZ’.
   3. It is recommended to set the ADV velocity range to ± 4 m/s, or larger.

3. For cable-head ADVs be sure to record the position and orientation of the ADV head relative to the ADV body (Figure 1). It is recommended that this information be stored in a <vec_filename>.userdata.json file (e.g., for a data file vector_data_imu01.VEC, the userdata file should have the name vector_data_imu01.userdata.json). json is a simple text-based data format. DOLfYN expects the format of this file to be a single dict-like container (i.e., the file should start with { and end with }, that contains 'name': value pairs, for example:

   {"body2head_rotmat": [[ 0, 0, 1],
                         [ 0,-1, 0],
                         [ 1, 0, 0]],
    "body2head_vec": [0.13, 0.04, 1.3],
    "declination": 14.3,
    "lat": 43.2,
    "lon": -123,
    "depth": 90,
    "inds_range": [1000, 5000]
   }
   

You can store any information you want to attribute with the binary .VEC file in this file. One advantage of creating the .userdata.json file is that this file is read when you read the .VEC file using the read_nortek() function, and the name-value pairs are added to the props attribute (a dictionary) of the returned ADVraw data object.

Figure 1) Coordinate systems of the ADV body (magenta) and head (yellow). The \(\hat{x}^\mathrm{head}\) -direction is known by the black-band around the transducer arm, and the \(\hat{x}^*\) -direction is marked by a notch on the end-cap (indiscernible in the image). The cyan arrow indicates the body-to-head vector, \(\vec{\ell}_{head}^*\) . The perspective slightly distorts the fact that \(\hat{x}^\mathrm{head} \parallel - \hat{z}^*\) , \(\hat{y}^\mathrm{head} \parallel -\hat{y}^*\) , and \(\hat{z}^\mathrm{head} \parallel -\hat{x}^*\) .

The other major advantage of using these userdata.json files, is that many property names are special in that they are used by DOLfYN in later processing steps. The property names that are recognized and used by DOLfYN to provide specific information are:

body2head_rotmat

The rotation matrix (a 3-by-3 array) that rotates vectors in the ADV’s pressure case coordinate system (i.e., the ‘body’), to the ADV head coordinate system. For fixed-head ADVs this is the identify matrix, but for cable-head ADVs it is an arbitrary unimodular (determinant of 1) matrix.

body2head_vec

The 3-element vector that specifies the position of the ADV head in the ADV pressure case’s coordinate system (Figure 1). This property must be in the props attribute of the data object in order to do motion correction, so you might as well store it in the userdata.json file.

declination

This is the magnetic declination at the measurement site. If it is in the props attribute, it will be incorporated into the orientation matrix when inst2earth() is called so that all rotations are to/from a ‘True’ north coordinate system, rather than a ‘magnetic’ north coordinate system.

Data processing

After making ADV-IMU measurements, the DOLfYN package can perform motion correction processing steps on the ADV data. Assuming you have created the .userdata.json file and added the body2head_rotmat and body2head_vec attributes to it, motion correction is fairly simple, you can either:

1. Utilize the DOLfYN api perform motion-correction processing explicitly in Python:

   from dolfyn.adv import api as adv
   

   1. Load your data file, for example:

      dat = adv.read_nortek('vector_data_imu01.vec')
      

   2. Then perform motion correction:

      adv.motion.correct_motion(dat, accel_filtfreq=0.1) # specify the filter frequency in Hz.
      

2. For users who want to perform motion correction with minimal Python scripting, the motcorrect_vectory.py script can be used. So long as DOLfYN has been installed properly, you can use this script from the command line in a directory which contains your data files:

   $ python motcorrect_vector.py vector_data_imu01.vec
   

   By default this will write a Matlab file containing your motion-corrected ADV data in ENU coordinates. Note that for fixed-stem ADVs (no cable-head), the standard values for body2head_rotmat and body2head_vec can be specified by using the --fixed-head command-line parameter:

   $ python motcorrect_vector.py --fixed-head vector_data_imu01.vec
   

   Otherwise, these parameters should be specified in the .userdata.json file, as described above.

   The motcorrect_vector.py script also allows the user to specify the accel_filtfreq using the -f flag. Therefore, to use a filter frequency of 0.1Hz (as opposed to the default 0.033Hz), you could do:

   $ python motcorrect_vector.py -f 0.1 vector_data_imu01.vec
   

   It is also possible to do motion correction of multiple data files at once, for example:

   $ python motcorrect_vector.py vector_data_imu01.vec vector_data_imu02.vec
   

   In all of these cases the script will perform motion correction on the specified file and save the data in ENU coordinates, in Matlab format. Happy motion-correcting!

After following one of these paths, your data will be motion corrected and it’s .u, .v and .w attributes are in an East, North and Up (ENU) coordinate system, respectively. In fact, all vector quantities in dat are now in this ENU coordinate system. See the documentation of the correct_motion() function for more information.

A key input parameter of motion-correction is the high-pass filter frequency that removes low-frequency bias drift from the IMU accelerometer signal (the default value is 0.033Hz, 30second period). By default, DOLfYN uses a value of 0.03 Hz. For more details on choosing the appropriate value for a particular application, please see [Kilcher_etal_2016].

[Kilcher_etal_2016]

Kilcher, L.; Thomson, J.; Talbert, J.; DeKlerk, A.; 2016, “Measuring Turbulence from Moored Acoustic Doppler Velocimeters” National Renewable Energy Lab, Report Number 62979.