SkyRadar Blog

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.

This article dives into the critical role of zero-velocity filters in air traffic control, highlighting how they improve radar accuracy by eliminating stationary objects from the radar display. A video shows an implementation with SkyRadar FreeScopes.

Explore Doppler filtering in radar imaging using SkyRadar's FreeScopes ATC II and SkySim. See how it refines target identification and reduces false alarms.

SkyRadar introduces an advanced object tracking methodology featuring both the linear Kalman filter and the interacting multiple model (iMM) filter. While the linear Kalman filter primarily focuses on longitudinal object positioning, the iMM algorithm expands capabilities to include lateral positioning, enhancing tracking robustness.

This video illustrates how to monitor moving targets through various alternative algorithms. It provides an easy to follow introduction into the setup of a radar block diagram and its scopes (A-Scope, PPI) using previously recorded IQ data.

This video compares the MTD algorithm and the Kalman filter. The use case is a moving target, in this particular case a person moving zig zag in front of SkyRadar's 8 GHz NextGen Pulse radar.

This article presents the Kalman filter in text and video. It is implemented in FreeScopes ATC II and works with live data, recorded data and simulation.

Apart of the linear Kalman filter, SkyRadar also presents an implementation of the interacting multiple model (iMM) filter for object tracking.

One of the most powerful statistical estimation techniques, which is widely applied in navigation, radar tracking, satellite orbit determination, autonomous driving, and many other fields is the Kalman filter. This digital filter provides a quite accurate estimation of the next state (position, movement, temperature, etc.) from any possible noisy input signal, in real time, which makes it very suitable for radar navigation and tracking purposes.