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radar-signal-processing

Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.

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Install command

npx skills add https://github.com/benchflow-ai/skillsbench --skill radar-signal-processing

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benchflow-ai/skillsbench

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UpdatedMay 3, 2026 at 18:42

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SKILL.md

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name

radar-signal-processing

description

Radar Signal Processing

Get from a raw binary I/Q capture to a clean 1-D phase trace that downstream vital-signs / motion analysis can consume.

Pipeline (do every step in order)

Parse binary I/Q to complex samples. Use the JSON/YAML sidecar to determine format — never assume. See references/iq-formats.md.
(FMCW only) Range FFT across fast time → 2-D range matrix. CW skips this step.
Remove static clutter. Subtract the temporal mean:
- CW: iq -= iq.mean()
- FMCW: R -= R.mean(axis=0, keepdims=True) on the range matrix
(FMCW only) Pick the subject range bin. See references/range-bin.md.

Extract phase with unwrap:

phase = np.unwrap(np.angle(iq_or_bin))
phase -= phase.mean()

If fs >= 500 Hz and downstream needs sub-Hz filtering, decimate first:

from scipy.signal import decimate
phase_ds = decimate(phase, q=int(fs/50), ftype='iir', zero_phase=True)
fs_new = fs / int(fs/50)   # ~50 Hz target

Critical rules

rule	why (short)
Use phase, not magnitude	1 mm motion at 24 GHz ≈ 1 rad; magnitude is ~40 dB worse SNR
Clutter removal goes before `np.angle`	DC offset anchors phase off zero, eats the unwrap budget
Never design a 0.1 Hz filter against a 2 kHz signal	SciPy biquad silently NaNs; decimate to ~50 Hz first
Never `argmax(magnitude)` across all range bins	DC bin (bin 0) and static reflectors dominate — restrict to a physical subject-range window

When things go wrong

If the output is garbage, walk through references/debugging.md in order — it's fast and catches most ingestion/SNR bugs.

Not in scope

Pulse/UWB time-of-flight, MIMO angle-of-arrival, Doppler-only gesture radar.

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Source

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Electronics Engineers, Except ComputerArchitecture and Engineering Occupations17-2072L4

name

radar-signal-processing

description

Radar Signal Processing

Get from a raw binary I/Q capture to a clean 1-D phase trace that downstream vital-signs / motion analysis can consume.

Pipeline (do every step in order)

Parse binary I/Q to complex samples. Use the JSON/YAML sidecar to determine format — never assume. See references/iq-formats.md.
(FMCW only) Range FFT across fast time → 2-D range matrix. CW skips this step.
Remove static clutter. Subtract the temporal mean:
- CW: iq -= iq.mean()
- FMCW: R -= R.mean(axis=0, keepdims=True) on the range matrix
(FMCW only) Pick the subject range bin. See references/range-bin.md.

Extract phase with unwrap:

phase = np.unwrap(np.angle(iq_or_bin))
phase -= phase.mean()

If fs >= 500 Hz and downstream needs sub-Hz filtering, decimate first:

from scipy.signal import decimate
phase_ds = decimate(phase, q=int(fs/50), ftype='iir', zero_phase=True)
fs_new = fs / int(fs/50)   # ~50 Hz target

Critical rules

rule	why (short)
Use phase, not magnitude	1 mm motion at 24 GHz ≈ 1 rad; magnitude is ~40 dB worse SNR
Clutter removal goes before `np.angle`	DC offset anchors phase off zero, eats the unwrap budget
Never design a 0.1 Hz filter against a 2 kHz signal	SciPy biquad silently NaNs; decimate to ~50 Hz first
Never `argmax(magnitude)` across all range bins	DC bin (bin 0) and static reflectors dominate — restrict to a physical subject-range window

When things go wrong

If the output is garbage, walk through references/debugging.md in order — it's fast and catches most ingestion/SNR bugs.

Not in scope

Pulse/UWB time-of-flight, MIMO angle-of-arrival, Doppler-only gesture radar.