process op• Data kinds: beatcube → beatcube
• Call: import fullseye as fs; fs.ledger.fmcw_window_apply(cube, window='hann', axis='range') (implementation को सीधे call करना हो तो import rangedoppler; rangedoppler.fmcw_window_apply(cube, window='hann', axis='range'), registry से लेना हो तो opsrangedoppler.get("fmcw_window_apply"))
beat cube के range और/या Doppler axis के साथ periodic window लगाता है।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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 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 (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• fmcw_range_doppler — py -3.11 examples/fmcw_range_doppler.py
beatcube को input के रूप में लेते हैं)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. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.