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 explains in detail the 2 Dimensional CFAR.

The Spectrogram looks at the received power from the targets. It represents the first one-dimensional Fourier transform and is given in a logarithmic scale. The received power spectrum allows to detect moving targets.

The 24 GHz Radar allows us to do play in the frequency band of 5G. In the following video we apply the Moving Target Indication Algorithm, with and without post-processing.

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.

The Standing Person Recognition Algorithm – SPRA provides the capability to detect reflections coming from a standing target (person), which exists in a stationary environment.  It is included in SkyRadar's FreeScope ATC I.

The Moving Target Detection is a highly reliable algorithm for detection of moving targets and temporarily non-moving targets in the constant presence of clutter. It is implemented in SkyRadar's ATC I Module.

The Moving Target Indication algorithm provides the capability to detect reflections coming from targets in motion with respect to a standing background environment, such as ground, walls, parked cars, and so on.