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Does your project require high-precision, cutting-edge distance measurement? Or maybe speed, motion, or gesture-sensing? We're not talking about simple ultrasonic or even infrared here, but 60GHz radar! Well say hello to the SparkFun A111 Pulsed Radar Breakout! The A111 is a single-chip solution for pulsed coherent radar (PCR) and comes complete with an integrated antenna and an SPI interface capable of clock speeds of up to 50MHz. Though the A111's primary use case is distance-sensing, it also supports applications in gesture, motion, material, and speed-detection at distances of up to two meters.
SparkFun Pulsed Radar Breakout for the A111 includes a 1.8V regulator, voltage-level translation, and it breaks out all the pins of the pulsed radar sensor to both 0.1-inch and Raspberry Pi-friendly headers. The Pulsed Radar Breakout is designed to sit directly on top of a Raspberry Pi but it doesn't span all 40 (2x20) pins of a Raspberry Pi B+ (or later), but the 26-pin -- 2x13 -- header should be compatible with any Pi. However, we have intentionally left the 2x13 header separated from the breakout in case you wanted to manually wire it to another an ARMv7 or ARM Cortex-M4 platform (since the closed-source A111 SDK currently only supports these architectures).
Acconeer has developed a visualization tool written in Python that demonstrates data collection in real time. It's called the Acconeer Exploration Tool and is an incredible resource when first starting out with the A111 Pulsed Radar. As an example it will graph distance or presence sensing, giving you a count of the number of sweeps, which communication port (SPI or I2C) data is being sent through, and much much more. The tool supports both Windows and Linux and requires Python version 3.6 or later. This tool is available through their Github Repository, go on over a take a look!
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Skill Level: Noob - Some basic soldering is required, but it is limited to a just a few pins, basic through-hole soldering, and couple (if any) polarized components. A basic soldering iron is all you should need.
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Skill Level: Experienced - You will require a firm understanding of programming, the programming toolchain, and may have to make decisions on programming software or language. You may need to decipher a proprietary or specialized communication protocol. A logic analyzer might be necessary.
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Skill Level: Rookie - You may be required to know a bit more about the component, such as orientation, or how to hook it up, in addition to power requirements. You will need to understand polarized components.
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Based on 4 ratings:
1 of 2 found this helpful:
I have been able to get accurate readings from 8in - 24in away from the sensor. Anything under 8in or above 24in outputs random values. I have used the Raspberry Pi 3B+ and 4B with both the acconeer_rpi_sparkfun 1.10.0 and 2.0 from their Developer site as well as the instructions from the Getting Started Guide where you combine the SparkFun GitHub files with the acconeer_rpi_xc111 packages, again with versions 1.9.1, 1.10.0, and 2.0 - for the combinations that actually return data (some come up with clock error) it is as described above - valid only between 8in and 24in. For the price of this sensor I do not feel it is worth it.
I purchased it on a whim to see if I could use it to perform inverse SAR imaging of modest-sized objects in a compact indoor range, and was happy to learn the IQ service coherence is plenty adequate to support that. I setup my “range” using a (slow, 1.8rpm) turntable/ground plane illuminated from short ranges (1-1.5m slant range); I used a lens Acconeer offers to get some antenna gain (and keep more of the beam on the ground plane - was able to get a very clean noise image with no object present, even indoors...) and operated the radar at 75 scans/s (Acconeer terminology) covering the maximum 0.75m allowed range span (streaming mode, no decimation, profiles 1 or 2, low-pass cutoff ratio 0.04, 32 pulse averages), 2500 scans total over a 360 degree aperture. Radiated power is a regret so ranges are kept short and objects that can be imaged are relatively small. After graduating from soup cans (top hat) etc, old metal Tonka toy trucks make a nice object, particularly ones with beds forming natural corner reflectors...
So, just some encouragement for someone wanting to explore radar, and willing to explore an alternative to FMCW chirp techniques (e.g., MIT coffee can radar). ISAR is not trivial (I’m an EE-’78) but backprojection is pretty straightforward and Acconeer’s design and IQ service provide range-resolved complex data with minimal effort. Hats off to Acconeer for a straightforward SDK, and working with Sparkfun to make the part available using the Raspberry Pi platform.