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https://github.com/ruvnet/RuView
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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.
39 lines
1.5 KiB
Rust
39 lines
1.5 KiB
Rust
//! Criterion benchmark for the backprojection reconstruction kernel.
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//! `cargo bench -p wifi-densepose-sar` reports MEASURED throughput --
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//! see the crate README for the last recorded numbers.
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#![allow(missing_docs)]
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use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
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use wifi_densepose_sar::geometry::linear_aperture;
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use wifi_densepose_sar::measurement::{simulate_measurement, FrequencySweep, ScatteringTarget};
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use wifi_densepose_sar::reconstruct::{backproject, VoxelGrid};
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use wifi_densepose_sar::Point3;
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fn backprojection_benchmark(c: &mut Criterion) {
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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 targets = vec![
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ScatteringTarget::new(Point3::new(0.0, 2.0, 0.0), 1.0),
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ScatteringTarget::new(Point3::new(0.3, 1.8, 0.1), 0.6),
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];
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let measurement = simulate_measurement(&poses, &sweep, &targets, 0.01, 7);
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let mut group = c.benchmark_group("backproject");
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for &voxels_per_axis in &[8usize, 16, 32] {
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let grid = VoxelGrid::new(
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Point3::new(-0.5, 1.5, -0.5),
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1.0 / voxels_per_axis as f64,
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voxels_per_axis,
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voxels_per_axis,
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voxels_per_axis,
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);
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group.bench_with_input(BenchmarkId::from_parameter(grid.len()), &grid, |b, grid| {
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b.iter(|| backproject(&measurement, &poses, &sweep, grid));
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});
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}
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group.finish();
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}
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criterion_group!(benches, backprojection_benchmark);
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criterion_main!(benches);
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