ruv
d781f20e1a
New standalone leaf crate implementing the synthetic-aperture-radar reconstruction primitive a handheld through-wall RF imaging device would need: a stepped-frequency multi-position complex forward measurement simulator, delay-and-sum backprojection reconstruction, point-cloud extraction, and closed-form range/cross-range resolution + antenna-pose coherence-budget formulas checked against the reconstruction's actual behavior in tests/physics_validation.rs. Motivated by comparing this repo against Applied Electrodynamics' "WaveSight" launch. Scoped explicitly below ADR-278's RISE/DiffRadar/ GeRaF reproduction gates: this is the bare measurement-model + backprojection primitive, not a reproduction of any published system or a claim about real hardware capability. Every number is SYNTHETIC/L0 (ADR-282) -- no wideband RF hardware backs this crate. 24 tests (21 unit + 3 integration), 0 failed, clippy-clean. Adds a tutorial walkthrough and MEASURED backprojection benchmark numbers.
wifi-densepose-sar
Coherent wideband RF tomography research crate (ADR-283): 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: 24 tests (21 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 | 1.47 ms | ~348,000 voxels/s |
| 4,096 | 10.4 ms | ~394,000 voxels/s |
| 32,768 | 73.5 ms | ~446,000 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.