[mdabydeen]

Hey Rahad, picking up #19. I converted the manual for i in 0..5 loops in UnscentedKalmanFilter2D::predict and ::update to .iter().zip() and .fold(), over the fixed-size sigma point arrays.

What changed

• The propagate, map, and cross-covariance loops zip their source arrays instead of indexing them.
• The weighted sums (state mean, predicted measurement, innovation, cross-covariance) fold over the zipped arrays.
• Summation order is unchanged, so the math is the same.

Why it is safe

• No heap allocation: every iterator is a slice over a stack array, so no_std still holds.
• A new test_ukf_predict_update_characterization pins the exact output of a predict + update pair to 1e-12 and passes bit-for-bit on the refactor.
• generate_sigma_points is untouched. Issue refactor(unscented): use idiomatic iterator zip for sigma point propagation and weighted sum #19 scopes the work to predict and update.

Verification

All four pass on my machine:

cargo fmt --check
cargo clippy --all-targets --all-features -- -D warnings
cargo test --all-features
cargo check --no-default-features --target thumbv7em-none-eabihf

Let it run on CI and I'll take any follow-ups. Thanks.

[mdabydeen]

Picking this one up in #20. The 0..5 loops in predict and update are now .zip() + .fold(), and a characterization test pins the numerics so the refactor stays bit-for-bit. no_std and the bare-metal build hold.

[rahadbhuiya]

Reviewed the changes. The zero-allocation iterator pipelines (.zip() / .fold()) are very clean and idiomatic, and the 1e-12 characterization test gives great regression confidence.

LGTM! Verified across bare-metal targets.