SkyRadar Blog

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.

The following video shows the algorithms Moving Target Indication (MTI) , MTI with post processing, Moving Target Detection (MTD) as well as MTD with Doppler filter, applied on raw data of a 24 GHz FMCW.

This article introduces SkyRadar's Monitoring Training System for ATSEP. It trains ATSEP on rapidly locating errors or points of system degradation, on rapidly fixing them and keeping downtimes short.

The following video explains in detail the 2 Dimensional CFAR.

NextGen implementation and application test by Ulisses Sodré de Almeida, Edasim, Brazil.

This video show several blocks in the digital signal processing chain that allow to enhance the visibility and tracking of moving targets. We show CFAR, Moving Targete Detection with Doppler Filtering, a Heatmap and the I and Q signals.

Within minutes we can replace the 8 GHz Pulse unit on top of the radar box with a a 24 GHz FMCW radar. We can analyze the FMCW with FreeScopes. Here is how it works.

In 2021, SkyRadar introduced the NextGen Training Radar Suite. It featured the 8 GHz Pulse radar. Now several extensions are available like a pseudo-noise radar (PSR), or the FMCW. This concept turns the NextGen into a platform, allowing to add virtually any radar, sonar or sensor.