process op• Data kinds: beatcube → image2d
• Call: import fullseye as fs; fs.ledger.range_doppler_map(cube, combine='incoherent', antenna=None, normalize=False) (to call the implementation directly, import rangedoppler; rangedoppler.range_doppler_map(cube, combine='incoherent', antenna=None, normalize=False); from the registry, opsrangedoppler.get("range_doppler_map"))
The 2-D FFT of a beat cube -> a (n_doppler, n_range) magnitude map.
Fast time transforms to range (last axis, not shifted: bin j is j * c*f_s/(2*S*N_s) metres, and a physical range is always positive so the whole [0, f_s) band is used). Slow time transforms to velocity (middle axis, fftshifted so the map is centred on zero velocity: bin i is (i - N_c//2) * lambda/(2*N_c*T_c) metres per second, positive = receding).
The antenna axis is collapsed by *combine*: "incoherent" (default) is the mean of the magnitudes, which is angle independent and therefore the right default for detection; "coherent" is the magnitude of the mean, i.e. a beam pointed at boresight, which attenuates an off-boresight target on purpose. antenna=k uses element k alone. For a single-element cube all three agree exactly.
normalize=True divides by N_c * N_s, so a bin-centred target of amplitude a peaks at exactly a (measured: 1.0 for a unit target, absolute error 0.0). The default False keeps the raw FFT magnitude.
No window is applied — compose fmcw_window_apply first if you want one. The output is a plain 2-D float64 array, so every 2-D operator in Fullseye (threshold, morphology, labelling, blob measurement — the pieces a CFAR detector is made of) applies to it directly.
Raises ValueError: a real-valued cube (it would put a mirror ghost of every target at a fabricated range), fewer than 2 chirps or 2 samples, an out-of-range *antenna* index, an unknown *combine*, a cube over the element cap, an FFT that overflows to NaN, or NaN/Inf on the way in.
• fmcw_range_doppler family guide
• 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.
• fmcw_range_doppler — py -3.11 examples/fmcw_range_doppler.py
image2d as input)process)fmcw_window_apply · range_doppler_peaks · fmcw_range_profile
*Provenance: rangedoppler.py — RANGEDOPPLER operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.