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Building an Offline Radar Workflow from Scratch

June 17, 2026 · The Clime Team
Building an Offline Radar Workflow from Scratch

Developing an offline radar processing workflow involves several key steps: data acquisition, preprocessing, signal processing, target tracking, and visualization. By leveraging open-source tools and real-time operating systems, you can build an efficient and effective system tailored to your specific needs.

1. Data Acquisition

The first step in building an offline radar workflow is to acquire raw radar data. This data is typically captured using radar sensors that output analog-to-digital converter (ADC) data. For instance, Texas Instruments (TI) mmWave radar boards can be used to collect such data. To record this data, you can utilize the DCA1000EVM capture card, which interfaces with the radar board and stores the ADC data along with metadata. The recorded data can then be used for offline processing. (mathworks.com)

2. Preprocessing

Once the raw ADC data is acquired, preprocessing is essential to prepare the data for further analysis. This stage involves filtering out noise, correcting for any distortions, and organizing the data into a format suitable for processing. For example, the OpenRadar library provides tools for interacting with and processing radar data, specializing in MIMO mmWave radars. This open-source library can be instrumental in the preprocessing phase, offering functionalities to handle raw radar data effectively. (github.com)

3. Signal Processing

Signal processing transforms the preprocessed data into meaningful information. This step includes range processing, Doppler processing, and angle of arrival (AoA) estimation. The pyRadar data processing pipeline outlines a comprehensive approach to signal processing, detailing each stage from hardware acquisition through signal processing to target tracking. This pipeline is designed to handle both offline batch processing and real-time streaming modes, making it versatile for various applications. (deepwiki.com)

4. Target Tracking

After signal processing, the next step is target tracking, which involves identifying and following objects of interest over time. Implementing a robust tracking algorithm is crucial for accurate detection and monitoring. For real-time applications, utilizing a real-time operating system (RTOS) like VxWorks can be beneficial. VxWorks offers deterministic task scheduling and interrupt handling, ensuring that the processing pipeline meets strict real-time deadlines. This setup is particularly useful in defense and surveillance systems where timely and reliable target tracking is essential. (vxworks6.com)

5. Visualization

The final step is to visualize the processed data and tracking results. This can be achieved by setting up a local server to host a web-based interface that displays radar data and tracking information. Tools like Virtual Radar Server can be configured to run offline, providing a user-friendly interface for monitoring radar data. By integrating this with a local OpenStreetMap tile server, you can create an interactive map that overlays radar data, enhancing situational awareness. (steamforge.net)

Conclusion

Building an offline radar processing workflow from scratch requires careful planning and integration of various components, including data acquisition, preprocessing, signal processing, target tracking, and visualization. By utilizing open-source tools and real-time operating systems, you can develop a customized system that meets your specific requirements. This approach not only provides flexibility but also leverages community-driven resources to enhance the capabilities of your radar processing workflow.

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