# Rts Smoother > Use when you must run a fixed-interval Rauch-Tung-Striebel (RTS) smoother over a stored forward Kalman-filter output for a discrete constant-velocity model in SI units: propagate the position and velocity state with the constant-velocity transition matrix, run the linear forward pass that stores the predicted and filtered means and covariances at every step, then execute the backward recursion with the smoother gain to combine each filtered estimate with the future measurements. Produces the smoothed state history, the smoother gains, the smoothed covariance history and the covariance reduction verdict that gate offline trajectory reconstruction and post-processing of navigation data. Trigger: rts smoother, rauch-tung-striebel, fixed-interval smoothing, backward pass, smoothed state, offline trajectory reconstruction, post-processing navigation data, constant-velocity model. - Skill: `ashfordeou/rts-smoother` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ashfordeou/rts-smoother` - Raw SKILL.md: https://api.skillmd.com/api/skills/ashfordeou/rts-smoother/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: Apache-2.0 - Author: ashfordeOU (https://skillmd.com/u/ashfordeou) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ashfordeou/rts-smoother --- # Rauch-Tung-Striebel Smoother (gnc-autonomy/estimation-filtering/rts-smoother) Use when the task is fixed-interval smoothing of a stored forward Kalman-filter history: a constant-velocity forward pass produces the predicted and filtered means and covariances at every measurement epoch, and the Rauch-Tung-Striebel (RTS) backward recursion then refines each filtered estimate with the measurements that came after it, giving the optimal smoothed state that uses all measurements, past and future. This leaf implements that pair in pure Python, stdlib only, deterministic and offline. It is the offline batch complement to gnc-autonomy/navigation/kalman-filter-design (the forward filter whose stored outputs feed the smoother) and pairs with gnc-autonomy/estimation-filtering/extended-kalman-filter and gnc-autonomy/estimation-filtering/unscented-kalman-filter on the nonlinear side. It never touches the live navigation state: smoothing is post-processing only, distinct from the cheap online tracking of alpha-beta-filter and the attitude fusion of complementary-filter, and it does not smooth raw flight-test traces with moving averages (flight-test-operations/planning/flight-test-data-reduction owns that). ## Domain quick reference - State x = [position, velocity] in SI units (m, m/s). Constant- velocity transition F = [[1, dt], [0, 1]] over the step dt (s), measurement model H = [[1, 0]], measurement noise variance r (m2). - Discrete process noise: Q = q * [[dt^4/4, dt^3/2], [dt^3/2, dt^2]] where q is the continuous acceleration noise intensity (m2/s3). - Forward predict: x_pred = F x_filt, P_pred = F P_filt F^T + Q. - Forward update: innovation y = z - H x_pred, innovation variance S = H P_pred H^T + r, gain K = P_pred H^T / S (2x1), x_filt = x_pred + K y, and the Joseph-form covariance P_filt = (I - K H) P_pred (I - K H)^T + K r K^T. - RTS backward recursion, k = n-2 down to 0, initialized at the last step with the filtered mean and covariance: smoother gain K_k = P_filt[k] F^T (P_pred[k+1])^-1 (2x2 inverse), then x_s[k] = x_filt[k] + K_k (x_s[k+1] - x_pred[k+1]) and P_s[k] = P_filt[k] + K_k (P_s[k+1] - P_pred[k+1]) K_k^T. - Boundary identity: at the final step the smoothed state and covariance equal the filtered values; every smoothed position variance sits at or below the filtered one, the fixed-interval smoothing benefit. - Units are SI throughout; the algorithms above are standard linear estimation methodology, summary-only. ## Workflow 1. Gather the stored measurement list (position samples), the model parameters dt, q, r, and the prior state x0 with covariance P0. dt must be positive, q non-negative, r positive, x0 length 2, P0 2x2, and at least two measurements present. 2. Run forward_kalman(measurements, dt, q, r, x0, p0). It returns one record per step with x_pred, P_pred, x_filt, P_filt, the innovation and its variance; each record also carries the step dt so the smoother can rebuild the transition matrix. 3. Inspect the forward history, for example the last filtered state and covariance, to confirm the filter behaved before smoothing. 4. Run rts_smooth(fwd_results). It returns (smoothed_states, smoothed_covs, gains): three lists of length n; gains[k] is the 2x2 smoother gain leaving step k and the final entry is a zero placeholder. 5. Quantify the benefit with smoother_reduction(fwd_results, smoothed_covs), which returns max_reduction (largest relative drop in position variance), all_reduced and boundary_matches. 6. Report the smoothed position and velocity history with the reduced covariance for the offline trajectory reconstruction or navigation data post-processing task; keep the live filter untouched. 7. Confirm the deterministic checks with the contract test scripts/test_rts_smoother.py. ## Worked example dt = 1 s, q = 0.1 m2/s3, r = 25 m2, x0 = [0.0, 5.0] m, P0 = diag(100, 10), with the 10 position samples [2.1, 6.8, 11.9, 16.4, 21.2, 26.5, 30.9, 36.2, 41.4, 45.8] m. - Forward filtered at the last step k=9: position 45.8108 m, velocity 4.8570 m/s, position variance 8.8487, velocity variance 0.5941. - Innovation variance runs from 135.025 (first step) down to 38.697 at the final step as the filter converges. - Smoothed at k=0: position 2.2080 m, velocity 4.8275 m/s. The forward filtered position at k=0 was 2.6369 m, so the backward pass has pulled the start of the track onto the data line. - Smoothed at k=4: position 21.5444 m, velocity 4.8439 m/s, with smoothed variances 2.7726 and 0.3326, both clearly below the filtered values 12.7131 and 1.8860 at k=4. - Smoother gain at k=0: about [0.9983, 0.0034]^T; the smoothed state at k=9 equals the filtered state at k=9 exactly. - Covariance verdict: all_reduced True, boundary_matches True, max_reduction about 0.78. ## Verification - Confirm forward_kalman on the worked example returns the k=9 anchors (45.8108, 4.8570, 8.8487, 0.5941) within 0.01 and that rts_smooth returns the k=0 and k=4 anchors (2.2080, 4.8275, 21.5444, 4.8439, 2.7726, 0.3326) within 0.01. - Confirm the boundary identity: the smoothed state and covariance at the last step equal the filtered values within 1e-12. - Confirm the covariance reduction verdict: every smoothed position variance at or below the filtered value, boundary_matches True. - Structural identity: smoothing a perfectly noiseless constant-velocity ramp (measurements 5*k for k = 0..9, exact model q = 0, initial state on the ramp at the epoch before the first sample) returns smoothed velocities within 1e-9 of 5.0 m/s at every step. With process noise q = 0.1 an off-ramp initial state is a legitimate prior inconsistency, so the exact-model ramp is the identity check. - Confirm every non-physical input raises ValueError: dt 0 or negative, q negative, r 0, fewer than two measurements, x0 not length 2, P0 not 2x2, and a singular 2x2 inversion. - Run the contract test offline: python3 scripts/test_rts_smoother.py (34 tests, deterministic). ## Pitfalls - Running the smoother online: RTS is a fixed-interval offline batch post-processor over a stored forward Kalman history (smoothed state at the final step equals the filtered state); it never touches the live navigation state. - Smoothing with fewer than two measurements: the backward recursion needs at least two epochs and fewer raise ValueError; the smoother gain at the final step is a zero placeholder, not a real gain. - Quoting the smoothing benefit from a single step: every smoothed position variance sits at or below the filtered one and smoother_reduction reports all_reduced, boundary_matches and max_reduction (about 0.78 on the worked example); the boundary identity at the last step is the structural check. - Expecting the exact-model identity with process noise: the noiseless constant-velocity ramp (q = 0, initial state on the ramp) returns velocities within 1e-9 of 5.0 m/s; with q = 0.1 an off-ramp initial state is a legitimate prior inconsistency, not a bug. - Model parameters are validated: dt must be positive, q non-negative, r positive, x0 length 2, P0 2x2, and a singular 2x2 inversion raises ValueError. ## Related leaves - gnc-autonomy/navigation/kalman-filter-design: the forward discrete Kalman filter whose stored outputs this smoother consumes. - gnc-autonomy/estimation-filtering/extended-kalman-filter and gnc-autonomy/estimation-filtering/unscented-kalman-filter: nonlinear forward filtering that the linear RTS recursion does not cover. - gnc-autonomy/estimation-filtering/alpha-beta-filter and gnc-autonomy/estimation-filtering/complementary-filter: cheap online tracking and attitude fusion, the real-time alternatives to batch smoothing. - flight-test-operations/planning/flight-test-data-reduction: moving-average smoothing of raw flight-test traces, a different smoothing task with no state model. ## Behavior contract (gate 3) Run the deterministic contract test (stdlib unittest, offline): python3 scripts/test_rts_smoother.py The test covers the 2x2 matrix helpers (multiplication, add, sub, scale, transpose, inverse with singular rejection), the forward filter record structure, the worked-example anchors at k=9, k=4 and the final-step innovation variance 38.697, the smoothed anchors at k=0 and k=4, the smoothed-below-filtered covariance comparison at k=4, the boundary identity, the smoother gain anchor at k=0, the covariance reduction verdict, the noiseless-ramp identity at q = 0, and ValueError rejection of every non-physical input. It runs in well under a second. ## Compliance - ARP4754A frames the development-assurance context for navigation estimation software; it is referenced, not reproduced, and the estimation relations above are standard engineering methodology, summary-only per standards-map.yaml. - compliance: STANDARDS-REF, gated: false.