process op• 데이터 종류: beatcube → beatcube
• 호출: import rangedoppler; rangedoppler.fmcw_window_apply(cube, window='hann', axis='range')(또는 opsrangedoppler.get("fmcw_window_apply"))
비트 큐브의 거리축·도플러축을 따라 주기 창을 적용합니다.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
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*.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• fmcw_range_doppler — py -3.11 examples/fmcw_range_doppler.py
beatcube 를 입력으로 받는 것)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 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.