tb_range_doppler_map — 2D typed op

• 資料種類:beatcube → image

• 呼叫:fullseye.apply(img, "tb_range_doppler_map", a=0.5, b=0.5)(2-D 的模型是一張圖 + 兩個純量旋鈕 a,b∈[0,1])

tb_range_doppler_map: input → output

*圖為在 128×128 合成輸入上實際執行的輸出。左為輸入,右為輸出。點雲以俯視散點顯示(亮度 = z),一維序列為折線,體資料為沿 z 的最大值投影,影片為中間影格,複數影像為振幅;無法成像的回傳值直接顯示數值。*

*旋鈕 a 不改變輸出(實測: 0.1 / 0.5 / 0.9 相同)。*

*旋鈕 b 不改變輸出(實測: 0.1 / 0.5 / 0.9 相同)。*

階段(前置運算子 → 本運算子,由左至右):

▸ tb_range_doppler_map: 處理階段 (文件網站)

換別的影像(合成場景 / 照片 / 硬幣。上排為輸入,下排為對應輸出。旋鈕取預設):

▸ tb_range_doppler_map: 其他輸入影像 (文件網站)

用法

拍頻立方體的二維 FFT -> (n_doppler, n_range) 幅度圖。

> 以下的詳細說明為原文 —— 摘要與標題已翻譯。

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.

Typed bridge of the rangedoppler op range_doppler_map into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b are unused.

參考(範例資料、文獻)

• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。

• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。

在 Studio 中試試

下面的程式已確認可以執行(與圖相同的輸入)。在 Studio 說明中,此區塊會變成按鈕,可當場載入並執行。

img_to_beatcube 0.50 0.50
tb_range_doppler_map 0.50 0.50

▸ 載入此管線  ·  載入並執行

可執行的範例(實際呼叫該運算子並已驗證的樣例)

下面的範例呼叫的是底層帳本運算子 range_doppler_map。此橋接運算子只是把同一實作適配為 fn(v, a, b) 慣例,行為完全相同(只是呼叫形式不同)。

• fmcw_range_doppler — py -3.11 examples/fmcw_range_doppler.py

型別可銜接的下一個運算子(可接受 image 作為輸入)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

同類別(typed)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample


*Provenance: ops.py — 2D 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.