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.
Skill by benchflow-ai
Install
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About
Use cases: data
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Files it touches
~/.claude/skills/radar-signal-processing
What's inside
- Skill name
radar-signal-processing- Description
- 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.
- License
- Apache-2.0
- Instructions
- 42 lines in SKILL.md · view on GitHub
Files (4)
- SKILL.md
- debugging.md · references/
- iq-formats.md · references/
- range-bin.md · references/
SKILL.md (first lines)
# 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) 1. **Parse binary I/Q to complex samples.** Use the JSON/YAML sidecar to determine format — never assume. See [references/iq-formats.md](references/iq-formats.md). 2. **(FMCW only) Range FFT across fast time** → 2-D range matrix. CW skips this step. 3. **Remove static clutter.** Subtract the temporal mean: - CW: `iq -= iq.mean()` - FMCW: `R -= R.mean(axis=0, keepdims=True)` on the range matrix 4. **(FMCW only) Pick the subject range bin.** See [references/range-bin.md](references/range-bin.md). 5. **Extract phase with unwrap:** ```python phase = np.unwrap(np.angle(iq_or_bin)) phase -= phase.mean() ``` 6. **If `fs >= 500 Hz` and downstream needs sub-Hz filtering, decimate first:** ```python 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](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.
From benchflow-ai/skillsbench, Apache-2.0.
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Signals
- Stars
- 1,870
- Forks
- 372
- Open issues
- 50
- Last commit (this folder)
- May 3, 2026 (5 months ago)
- Last release
- Jun 14, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/benchflow-ai/skillsbench
- Website
- www.skillsbench.ai
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
radar-signal-processing skill. Agent Mart. https://agentmart.dev/skills/benchflow-ai-radar-signal-processing (last updated 2026-10-24)