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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).
75 lines
3.3 KiB
Markdown
75 lines
3.3 KiB
Markdown
# wifi-densepose-sar
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Coherent wideband RF tomography research crate (ADR-287): synthetic
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stepped-frequency multi-position measurement simulation + delay-and-sum
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backprojection reconstruction of a 3D reflectivity field.
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**This is not a hardware capability.** It is the reconstruction primitive a
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handheld through-wall RF imaging device would need, validated against its
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own synthetic ground truth. Every number this crate produces is
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SYNTHETIC / evidence level L0 (ADR-282) until real wideband RF hardware
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(a VNA, SDR, or purpose-built radar front end) exists to feed it real
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measurements. See the crate-level doc comment in `src/lib.rs` for the full
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honesty boundary, and the tutorial at
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`docs/tutorials/coherent-rf-tomography-backprojection.md` for a walkthrough.
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## Quick example
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```rust
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use wifi_densepose_sar::{
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backproject, linear_aperture, simulate_measurement, FrequencySweep,
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Point3, ScatteringTarget, VoxelGrid,
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};
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let poses = linear_aperture(Point3::new(-0.5, 0.0, 0.0), Point3::new(0.5, 0.0, 0.0), 21);
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let sweep = FrequencySweep::new(2.0e9, 6.0e9, 32);
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let target = ScatteringTarget::new(Point3::new(0.0, 2.0, 0.0), 1.0);
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let measurement = simulate_measurement(&poses, &sweep, &[target], 0.01, 42);
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let grid = VoxelGrid::new(Point3::new(-0.3, 1.7, -0.3), 0.03, 21, 21, 21);
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let image = backproject(&measurement, &poses, &sweep, &grid);
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let (peak_location, peak_magnitude) = image.peak();
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println!("reconstructed target near {peak_location:?}, magnitude {peak_magnitude:.4}");
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```
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## Testing
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```bash
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cargo test -p wifi-densepose-sar --no-default-features
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cargo bench -p wifi-densepose-sar
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```
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`tests/physics_validation.rs` checks the reconstruction's actual behavior
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against the closed-form formulas in `resolution.rs` (range resolution,
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cross-range/synthetic-aperture resolution, and the antenna-pose coherence
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budget) rather than merely asserting them: 25 tests (22 unit + 3
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integration), 0 failed, clippy-clean.
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## Performance (MEASURED)
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`cargo bench -p wifi-densepose-sar`, 21 antenna poses × 32 frequency steps
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(672 measurement terms/voxel), rayon-parallelized over voxels, this
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machine, release profile:
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| Voxels | Median time | Throughput |
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|-------:|------------:|-----------:|
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| 512 | 300 µs | ~1.71M voxels/s |
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| 4,096 | 1.97 ms | ~2.08M voxels/s |
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| 32,768 | 14.5 ms | ~2.26M voxels/s |
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Scales as expected: each voxel's cost is independent (`O(poses × freqs)`
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per voxel, embarrassingly parallel), so throughput is roughly constant
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across grid sizes and total time scales linearly with voxel count.
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**Optimization (MEASURED, criterion regression detection, p < 0.001): ~4.4-4.5x
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faster** than the first-shipped implementation, across all three grid
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sizes. Frequencies in a [`FrequencySweep`](src/measurement.rs) are evenly
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spaced by construction, so the per-(pose, frequency) phase term is an
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arithmetic progression; `focus_at_point` now evaluates the phasor once per
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pose and advances it by a fixed complex-multiply step per frequency,
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instead of one `sin`/`cos` pair (`Complex64::from_polar`) per frequency —
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K trig evaluations become 2. Proven equivalent (not just faster) to an
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independently-reimplemented direct per-frequency reference in
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`reconstruct::tests::backprojection_incremental_rotation_matches_direct_per_frequency_computation`,
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across several sweep sizes and both on-target and off-target points.
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