SENSYS MagDrone R3 and R4 magnetometer data processing - ugcs/GeoHammer GitHub Wiki

The SENSYS MagDrone R3 is an ultra-portable magnetometer designed to be mounted on any UAV with a minimum payload capacity of 1 kg. It features two triaxial fluxgate magnetic sensors, a sensor bar, and an onboard data recorder. From serial number SN000200 onward, it includes built-in GPS and supports external GPS for improved geotagging—especially effective when paired with SkyHub and RTK-enabled drones.
The MagDrone R4 is an ultra light weight magnetometer with 5 triaxial Fluxgates to be attached to any UAV capable to carry 2 kg of payload. In opposite to the MagDrone R3, the MagDrone R4 is used for high resolution mapping in order to detect small and compact objects as well as structures in the ground, such as UXO or archaeological features.

MagDrone R3 Detection Capabilities:	MagDrone R4 Recommended applications:
* Small objects like F1 hand grenades (up to 0.5 m away)	* Searching for UXO (unexploded ordnance)
* Large UXOs like aerial bombs (detectable from several meters)	* Archaeology
Typical Applications:	* Surveying for any metal objects weighing a few hundred grams or heavier lying underground
* UXO detection in inaccessible, flooded, or mined areas
* Survey and surveillance missions
* Mine exploration
* Tracking and monitoring at flexible altitudes

Data used in this tutorial can be downloaded here: R3 and R4.
Data was gathered using a MagDrone R3 and MagDrone R4 magnetometer over the SENSYS - Magnetometers & Survey Solutions test range.

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1. Use the “Open files” toolbar button or drag and drop the required file for processing.

2. Use the “Select Area” button to choose desired survey area and click “Apply Crop”.

3. Choose “TMI” in data processing zone for further data processing.

4. Before applying the filters, we can grid the raw data to check if the data was recording correctly and we aren't missing any lines.

• First, we need to enter cell size for our grid. Choosing a number too little will take a lot of time as it needs to generate very small cells, therefore, depending on the survey size and type it's advisable to start with cell size 1/4th of the spacing between lines and gradually decreasing it. Lower cell size gives higher resolution but take more computing power. For our data set we chose the cell size 0.1.
  
• Second, we need to enter blanking distance. Blanking distance should be sufficient enough to cover areas between flight paths to make up for no-data zones. As our flights had less than 1.0 meter spacing between lines, we can enter 1 for complete blanking for our grid and click "Apply".

Gridded map	Parameters used in gridding

5. Let's apply Low-Pass filter. For our dataset we will apply 100 fiducials of cut-off wavelength and click “Apply".

6. Next step is to perform GNSS time-lag correction. In our case, the time-lag isn't as pronounced after checking the gridded data, but if we test by adding small values, we can see that after applying Time-Lag Correction of just 15 fiducials, anomalies look a bit better than previously:

7. After applying Time-lag correction, next step is to apply Running median filter. Using this filter will give us the amplitude of local magnetic anomalies relative to median value. We'll enter a window size of 1250 as we`re interested in smaller anomalies found in the survey area.

• Note, that applying smaller window will also make noise more prominent.

After applying our Running Median filter, we need to change our data view to "TMI_anomaly" as this will be our final result. Click on it and apply Gridding once again with the same parameters.

We have completed SENSYS MagDrone R3 data processing.

For comparison, we'll also process SENSYS MagDrone R4 data. By following the same steps as previously, we'll produce anomaly map from SENSYS MagDrone R4 magnetometer data:

If we need to place marks or export map as .tiff file, you can read about more here.