fmcw_range_profile — RANGEDOPPLER `process` op

• Data kinds: beatcube → signal

• Call: import rangedoppler; rangedoppler.fmcw_range_profile(cube, chirp=None, antenna=None, normalize=False) (or opsrangedoppler.get("fmcw_range_profile"))

Usage

Range-only profile: the fast-time FFT magnitude, averaged over the rest.

The 1-D marginal of :func:range_doppler_map — what a static scene needs,

and what a single chirp can give. Magnitudes are averaged (never the complex

values) over chirps and antennas, so the average is independent of the

target's velocity and angle: `|FFT|` does not rotate with the Doppler

phase, only the phase does.

Bin `j is j * c*f_s/(2*S*N_s) metres. With normalize=True` a

bin-centred target of amplitude `a peaks at exactly a`.

*chirp* / *antenna* select one slice instead of averaging. Returns a 1-D

float64 array of length `n_samples — a plain signal, so :mod:dsp` and

:mod:funct1d (`find_peaks, smooth_funct_1d_gauss, spectrum`)

apply to it directly.

Raises `ValueError: as :func:range_doppler_map`, plus an

out-of-bounds *chirp* index.

Detailed usage guide

• fmcw_range_doppler family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• fmcw_range_doppler — py -3.11 examples/fmcw_range_doppler.py

Ops the type connects to (they accept `signal` as input)

—

Same category (`process`)

fmcw_window_apply · range_doppler_map · range_doppler_peaks

*Provenance: rangedoppler.py — RANGEDOPPLER operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

fmcw_range_profile — RANGEDOPPLER process op