Dev Kit Weekly: Texas Instruments AWR1642BOOST mmWave Radar Sensor Eval Module

September 27, 2019 Brandon Lewis

 
This week we review the AWR1642BOOST mmWave Radar Sensor Evaluation Module from Texas Instruments. As you might expect, the kit is centered around a radar sensor of the same name, but that's probably about all that you'd expect from this advanced automotive radar prototyping kit.
 
The radar sensor itself is capable of scanning 4 GHz simultaneously in the 76 to 81 GHz spectrum bands, which equals twice the bandwidth of sensors on the market today. But the sensor goes above and beyond that to integrate an Arm Cortex-R4F and TI C674X DSP, which means all of the baseband and FMCW algorithm processing can be performed right on the CMOS sensor itself.
 
That's a first of its kind in the auto industry, packing all of the analog, digital, RF, and processing components you need on a single device. That means less space, less power, and of course, less cost.
 
The AWR1642BOOST is also extremely accurate thanks to integrated fractional phased locked loops (PLLs), which help it achieve very low error rates and high-resolution accuracy down to 4 cm. This makes the system great for prototyping external automotive radar applications with a range of up to 200 m, or in-cabin use cases like driver monitoring.
 
The best part is, with integrated antennas; CAN and CAN-FD interfaces; a UART-to-USB port; and support for the mmWave SDK replete with analog front-end (AFE) development tools, point cloud examples, and optimized algorithms, you can start detecting objects via radar almost immediately out of the box.
 
As always, you can win this kit by entering the raffle here: https://opensysmedia.formstack.com/forms/dev_kit_weekly_kit_raffle
 
If you're interested in learning more about the Texas Instruments AWR1642BOOST mmWave Radar Sensor Evaluation Module, visit http://www.ti.com/tool/AWR1642BOOST.
 
See you next week...

About the Author

Brandon Lewis

Brandon Lewis, Editor-in-Chief of Embedded Computing Design, is responsible for guiding the property's content strategy, editorial direction, and engineering community engagement, which includes IoT Design, Automotive Embedded Systems, the Power Page, Industrial AI & Machine Learning, and other publications. As an experienced technical journalist, editor, and reporter with an aptitude for identifying key technologies, products, and market trends in the embedded technology sector, he enjoys covering topics that range from development kits and tools to cyber security and technology business models. Brandon received a BA in English Literature from Arizona State University, where he graduated cum laude. He can be reached by email at brandon.lewis@opensysmedia.com.

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