Preparing instrument scanning data for school detection

The instrument scanning school detection algorithm partitions data into frames. It applies only to data collected using instrument scanning.

This page outlines the recommended steps for preparing instrument-scanned data for school detection. If you encounter problems detecting schools in instrument-scanned data, we strongly recommend following these steps.

1. Identify your variables
   1. Display the Dataflow window.
   2. Identify the position variable you intend to use (it is probably connected to the platform).
   3. Identify the acoustic variable you intend to use (it must be an H-mode or V-mode variable recorded by instrument scanning).

   If you do not have these two variables, you cannot continue with school detection.

2. Correct the data for motion (pitch and roll)—highly recommended

   Apply motion correction to a raw variable before applying other operators or performing analysis or processing.

   1. Create a virtual variable from your chosen acoustic variable using the Motion correction (Dunford method) operator.
   2. Select the chosen acoustic variable from the Operand 1 list.
   3. Select a roll variable from the Operand 2 list.
   4. Select a pitch variable from the Operand 3 list.
   5. On the Calibration page, in the Inherit calibration settings from: section, select the variable you nominated as Operand 1.

   The resulting virtual variable can then be processed and analyzed as normal.

   See Using the motion correction operator for more information.

   Other features that correct for vessel motion include heave compensation (applied to a platform), Motion range bitmap, and Multibeam roll at transducer.

3. Smooth the position data—optional
   1. Create a virtual variable from your chosen position variable using the Kalman GPS filter operator.
   2. Select the desired position variable on the Operands page of the Variable Properties dialog box.
   3. Set the degree of smoothing on the Smoothing page of the Variable Properties dialog box.
   4. Select the smoothed position variable on the Position page of the Platform Properties dialog box.

   The resulting virtual position variable contains data that has been smoothed with a Kalman filter.

   The Notes page of the Variable Properties dialog box for the virtual variable displays the operator statistics for the smoothing.

4. Add heading data to the platform—highly recommended
   1. On the Attitude page of the Platform Properties dialog box, select the heading data rather than a heading calculated from the cruisetrack.

You are now ready to detect 3D schools. See Creating 3D regions for more information.

Notes:

• Consider removing noise from the multibeam variable with noise removal operators.
• Consider excluding unwanted data with detected or virtual surfaces. See also About surfaces. Alternatively, use the Crop samples operator.

See also

Creating 3D regions
About school detection algorithms
3D school detection algorithms for instrument scanning
Detecting schools on multibeam data