Plots & Tracks in the PPI - Creating Clean Plots & Tracks from Live Data with SkyRadar's Inhouse Pulse Radar (Video)

This video shows how to extract plots and tracks from live radar raw data (IQ-data) with FreeScopes.

At the recent trade show in Geneva, I recorded a radar IQ data log while walking back and forth in front of our system. In this blog, I use that log to build a block diagram in FreeScopes—SkyRadar’s DSP and visualization environment—to extract plots and tracks from the raw radar signal.

The video illustrates each step of the process, from replaying the recorded data to configuring interactive blocks, applying a Kalman filter, and visualizing clean plots and tracks—even in a cluttered trade show environment.

If you can track a single person in such conditions, you can track almost anything.

Watch the Video

Explainer

The demonstration is not just about replaying data—it's a hands-on walkthrough of how modern radar signal processing works.

Plots represent raw radar detections. Each plot is essentially a blip: a point where the radar sees something reflecting the signal back. However, not all plots are relevant—some may be noise, static objects, or interference.
Tracks are a series of connected plots that the system interprets as a moving object. Tracking algorithms evaluate the behavior of plots over time to determine if they belong to the same moving target.
The Kalman filter plays a crucial role here. It’s a mathematical tool used to clean and predict movement. In the video, you’ll see how it filters out noise and disturbances, creating a smooth and reliable trajectory from noisy, jumpy plot data. This makes it ideal for real-time tracking—even in busy or cluttered environments like a trade show.
To ensure clarity in tracking, we also apply thresholding, which suppresses low-level clutter before the Kalman filter processes the data. This two-step cleanup allows the system to focus on real movements instead of background noise.

The outcome is a powerful visualization of how raw radar signals evolve into meaningful, actionable information.

Let's talk

Stay tuned to be always the first to learn about new use cases and training solutions.

Or simply talk to us to discuss your project.


Plots & Tracks in the PPI - Creating Clean Plots & Tracks from Live Data with SkyRadar's Inhouse Pulse Radar (Video) This video shows how to extract plots and tracks from live radar raw data (IQ-data) with FreeScopes. At the recent trade show in Geneva, I recorded a radar IQ data log while walking back and forth in front of our system. In this blog, I use that log to build a block diagram in FreeScopes—SkyRadar’s DSP and visualization environment—to extract plots and tracks from the raw radar signal. The video illustrates each step of the process, from replaying the recorded data to configuring interactive blocks, applying a Kalman filter, and visualizing clean plots and tracks—even in a cluttered trade show environment. If you can track a single person in such conditions, you can track almost anything. Watch the Video Explainer The demonstration is not just about replaying data—it's a hands-on walkthrough of how modern radar signal processing works. Plots represent raw radar detections. Each plot is essentially a blip: a point where the radar sees something reflecting the signal back. However, not all plots are relevant—some may be noise, static objects, or interference. Tracks are a series of connected plots that the system interprets as a moving object. Tracking algorithms evaluate the behavior of plots over time to determine if they belong to the same moving target. The Kalman filter plays a crucial role here. It’s a mathematical tool used to clean and predict movement. In the video, you’ll see how it filters out noise and disturbances, creating a smooth and reliable trajectory from noisy, jumpy plot data. This makes it ideal for real-time tracking—even in busy or cluttered environments like a trade show. To ensure clarity in tracking, we also apply thresholding, which suppresses low-level clutter before the Kalman filter processes the data. This two-step cleanup allows the system to focus on real movements instead of background noise. The outcome is a powerful visualization of how raw radar signals evolve into meaningful, actionable information. Let's talk Stay tuned to be always the first to learn about new use cases and training solutions. Or simply talk to us to discuss your project.