ruv
e4695d8c68
ADR-283 was already taken by ADR-283-ruview-community-metaharness-flywheel.md, merged to main before this branch's work started -- picked without checking against main's actual current ADR list. Renumbered to ADR-287, the next free slot after ADR-286 (the wifi-densepose-sar-harness ADR, no collision there). Updated every reference across the crate (Cargo.toml description, lib.rs/ geometry.rs/measurement.rs/pointcloud.rs/reconstruct.rs/resolution.rs doc comments, tests/physics_validation.rs), its README, the tutorial doc, CHANGELOG.md, and the workspace Cargo.toml's member comment. 25 tests still pass after the rename (doc-comment-only changes, no logic touched).
wifi-densepose-sar
Coherent wideband RF tomography research crate (ADR-287): synthetic stepped-frequency multi-position measurement simulation + delay-and-sum backprojection reconstruction of a 3D reflectivity field.
This is not a hardware capability. It is the reconstruction primitive a
handheld through-wall RF imaging device would need, validated against its
own synthetic ground truth. Every number this crate produces is
SYNTHETIC / evidence level L0 (ADR-282) until real wideband RF hardware
(a VNA, SDR, or purpose-built radar front end) exists to feed it real
measurements. See the crate-level doc comment in src/lib.rs for the full
honesty boundary, and the tutorial at
docs/tutorials/coherent-rf-tomography-backprojection.md for a walkthrough.
Quick example
use wifi_densepose_sar::{
backproject, linear_aperture, simulate_measurement, FrequencySweep,
Point3, ScatteringTarget, VoxelGrid,
};
let poses = linear_aperture(Point3::new(-0.5, 0.0, 0.0), Point3::new(0.5, 0.0, 0.0), 21);
let sweep = FrequencySweep::new(2.0e9, 6.0e9, 32);
let target = ScatteringTarget::new(Point3::new(0.0, 2.0, 0.0), 1.0);
let measurement = simulate_measurement(&poses, &sweep, &[target], 0.01, 42);
let grid = VoxelGrid::new(Point3::new(-0.3, 1.7, -0.3), 0.03, 21, 21, 21);
let image = backproject(&measurement, &poses, &sweep, &grid);
let (peak_location, peak_magnitude) = image.peak();
println!("reconstructed target near {peak_location:?}, magnitude {peak_magnitude:.4}");
Testing
cargo test -p wifi-densepose-sar --no-default-features
cargo bench -p wifi-densepose-sar
tests/physics_validation.rs checks the reconstruction's actual behavior
against the closed-form formulas in resolution.rs (range resolution,
cross-range/synthetic-aperture resolution, and the antenna-pose coherence
budget) rather than merely asserting them: 25 tests (22 unit + 3
integration), 0 failed, clippy-clean.
Performance (MEASURED)
cargo bench -p wifi-densepose-sar, 21 antenna poses × 32 frequency steps
(672 measurement terms/voxel), rayon-parallelized over voxels, this
machine, release profile:
| Voxels | Median time | Throughput |
|---|---|---|
| 512 | 300 µs | ~1.71M voxels/s |
| 4,096 | 1.97 ms | ~2.08M voxels/s |
| 32,768 | 14.5 ms | ~2.26M voxels/s |
Scales as expected: each voxel's cost is independent (O(poses × freqs)
per voxel, embarrassingly parallel), so throughput is roughly constant
across grid sizes and total time scales linearly with voxel count.
Optimization (MEASURED, criterion regression detection, p < 0.001): ~4.4-4.5x
faster than the first-shipped implementation, across all three grid
sizes. Frequencies in a FrequencySweep are evenly
spaced by construction, so the per-(pose, frequency) phase term is an
arithmetic progression; focus_at_point now evaluates the phasor once per
pose and advances it by a fixed complex-multiply step per frequency,
instead of one sin/cos pair (Complex64::from_polar) per frequency —
K trig evaluations become 2. Proven equivalent (not just faster) to an
independently-reimplemented direct per-frequency reference in
reconstruct::tests::backprojection_incremental_rotation_matches_direct_per_frequency_computation,
across several sweep sizes and both on-target and off-target points.