In air traffic control, managing radar clutter is essential for ensuring both safety and operational efficiency. This article provides an in-depth look at the use of clutter maps and clutter-map-subtraction techniques, which are employed to filter out stationary and irrelevant objects from radar displays. By focusing on these advanced filtering methods, the article aims to shed light on how radar systems are optimized to allow air traffic controllers to concentrate on tracking moving aircraft.

Reflections from Stationary Targets are a Challenge

Conventional radar systems emit radio waves that bounce off objects and return to the radar. The time it takes for the radio wave to return is proportional to the distance to the object. While this information is valuable, it also includes reflections from stationary objects or even dynamic objects that are not relevant to air traffic control. 

The radar might capture reflections from houses or mountains. Those are static. But even reflections from dynamic objects like wind turbines might disturb.

Subtracting Everything That is Not a Clutter

Eliminating everything which is not clutter is an essential feature in modern radar systems used for air traffic control. By eliminating clutter and focusing on moving aircraft, it significantly enhances situational awareness, allowing for safer and more efficient airspace management. 

Building a Clutter Map

In a first step we look at a clutter map. A clutter map is a digital map of everything. 

How does it work?

We simply run the "clutter recorder" during several revolutions. Ideally we do that during a moment in time when there is no target in the area to be analyzed.

When running the recording during several rotations, we can generate a more accurate clutter map. Normally the algorithm generates an averaged image over the various revolutions. A solution may be a mean value of the recorded clutter across various revolutions. Sometimes the recent data gets a higher weighting than its preceding logs. The user should be able to define the number of revolutions in the configurations of the clutter-map.

A video on the clutter map

In the following video you can witness the generation of a clutter map. We did not prepend a zero-velocity filter. In consequence a moving object which crossed the monitored area during the recording time is being recorded and appears several times. You can well see how the intensity of the moving object reduces over time, as the mean value over all rotations is continuously updated by the algorithm. You also see that the recent value has a higher weighting.


To generate a clean image for the radar operator, the clutter map needs to be deducted from the actual image. This works through clutter map subtraction as done in the video below.

You see that during the first revolution, the algorithm builds a clutter map. The A-Scope shows the amplitudes of the clutter. The PPI shows a snowy background.

From the second revolution onwards, the algorithm subtracts these clutter-signals. In consequence the moving target is well visible.

Actually, the target does not need to be moving. In contrast to the MTI algorithm or the subtraction of a zero velocity filter, such a clutter map also allows to see static targets, e.g. a parked vehicle on the runway.


The radar image shows only the moving targets. The image is freed of any clutter.

Things to Consider

The first video shows a moving object during the recording of the clutter maps. When recording a clutter map, the operator should make sure that no object is in the monitored area during the time of recording.

E.g. when creating the clutter map of an airport, the airport should be free of moving or static objects like aircraft or cars. When the clutter map is created you want only buildings and fixed objects on it. Parked or moving aircraft or vehicles are not part of the clutter. In most cases it will be helpful to prepend a zero velocity filter to the block diagram. But there ma be cases like wind turbines where you also want to record dynamic clutter.

The clutter map and clutter-map-subtraction are part of FreeScopes ATC II.  A-Scope and PPI are part of FreeScopes Basic I.

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