SkyRadar Blog

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.

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.