process op• Data kinds: beatcube → beatcube
• Call: import rangedoppler; rangedoppler.fmcw_window_apply(cube, window='hann', axis='range') (or opsrangedoppler.get("fmcw_window_apply"))
Apply a periodic window along the range and/or Doppler axis of a beat cube.
The sidelobes of a rectangular (unwindowed) transform are -13.3 dB, so a
strong target buries a weak one 20 dB down at a completely different range.
Windowing trades main-lobe width for sidelobe level; the published figures
(Harris 1978, Table 1) and the levels measured in this repository on a
single bin-centred target are:
========== ============== ============== ==================
window published PSL measured PSL measured -3 dB lobe
========== ============== ============== ==================
rect -13.3 dB -13.25 dB 0.885 bin
hann -31.5 dB -31.47 dB 1.438 bin
hamming -42.7 dB -42.45 dB 1.301 bin
blackman -58.1 dB -58.11 dB 1.641 bin
========== ============== ============== ==================
Measured by transforming each window on its own with 2^18-point zero padding
and taking the highest lobe past the first null — that *is* the definition of
peak sidelobe level, so these are the module's own numbers, not copied ones.
Hamming lands 0.25 dB off the published figure because the published one is
for the optimal 0.53836/0.46164 pair; the 0.54/0.46 coefficients written here
are the textbook ones and this is what they actually give.
What it buys, measured end to end: a target 45 dB below a strong one, seven
range bins away, is undetectable unwindowed (its cell sits 24.6 dB down
in the leakage skirt and is not even a local maximum) and becomes a clean
local maximum at -43.6 dB with `hann`. That comparison is step 4 of
`examples/fmcw_range_doppler.py`.
*axis* is named by role — `"range"` (fast time, the last axis),
`"doppler" (slow time, the middle axis) or "both"` — never by number,
because a transposed cube is the mistake this naming is defending against.
The window is *not* folded into :func:range_doppler_map: keeping it a
separate op is what lets the sidelobe table above be measured as a
difference, and keeps the transform op a pure 2-D FFT.
Returns a new complex cube of the same shape. Raises `ValueError` on a
real-valued or malformed cube, or an unknown *window* / *axis*.
• 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
beatcube as input)range_doppler_map · fmcw_range_profile · beamform_delay_sum · beamform_doa
process)range_doppler_map · 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.