Detection-first point clouds

The default 4D pipeline detects on range-Doppler power before estimating angle. This avoids allocating a dense range-Doppler-azimuth-elevation tensor when only a small number of cells contain targets.

RD power and detections

After MIMO timing compensation and array calibration,

\[ P[r,d]=\frac{1}{M}\sum_{m=0}^{M-1}|X[r,d,m]|^2. \]

CFAR produces range and Doppler source bins. For each surviving cell, nearby range/Doppler samples provide snapshots for the selected DoA method.

Coordinate conversion

The physical values are

\[ R = k_r\Delta R_\mathrm{bin}, \qquad v_r = \mathrm{velocityAxis}[k_d]. \]

With azimuth \(\theta\) and elevation \(\phi\), FLU coordinates are

\[ x=R\cos\phi\cos\theta,\qquad y=R\cos\phi\sin\theta,\qquad z=R\sin\phi. \]

PointCloud stores xyz, power, snr, radialVelocity, timestamp, frame id, and source bins (range, doppler, azimuth, elevation). to_numpy() returns columns x, y, z, power, snr, radialVelocity; provenance remains available on the typed object.

Dense research products

retainRangeDoppler, retainRangeAngle, and retainRangeDopplerAngle control intermediate products. The default RA visualization beamforms the strongest Doppler cell at each range. Full RDA is generated only when explicitly requested. Elevation remains available per detection even when a dense azimuth-only product is retained.

Ambiguities

Point coordinates are estimates inside the model’s unambiguous range and angular FOV. TDM profiles with calibrated overlap phase centres can resolve velocity aliases inside the raw chirp-rate Nyquist interval; the selected order is stored in frame metadata. Without that evidence the library preserves the aliased velocity rather than claiming a unique physical target. Application-specific range unwrapping remains outside v1.