Kalman local-linear-trend smoother body for scan_over().

Returns a scan body fn(xs, margin) computing the smoothed level mean (or its standard error) of a per-pixel local-linear-trend Kalman filter with a Rauch-Tung-Striebel smoothing pass over the t axis, batched over the chunk's pixels. This is the low-level building block; most users want kalman_smooth(), which wraps it. Use with scan_over(x, kalman_llt(...), direction = "bidir"); x is the observation stack, optionally list(x, r) with r a per-year relative observation-variance stack (Var(v_t) = sigma_obs^2 * r_t; r must be finite wherever y is observed).

Usage

kalman_llt(
  sigma_lvl,
  sigma_slp,
  sigma_obs = 1,
  output = c("mean", "sd"),
  robust_iters = 0L,
  robust_threshold = 3,
  robust_inflation = 100,
  kappa = 1e+07,
  out_dtype = "f32"
)

Arguments

sigma_lvl, sigma_slp, sigma_obs

Noise standard deviations (level disturbance, slope disturbance, observation).

output

"mean" (smoothed level) or "sd" (its standard error).

robust_iters

Robust reweighting passes (0 = plain smoother). Each pass inflates the level noise at years whose smoothed-level innovation exceeds robust_threshold MADs by robust_inflation.

robust_threshold, robust_inflation

Robust loop constants.

kappa

Diffuse-initialisation variance.

out_dtype

Output dtype the body casts to (align with scan_over(dtype = ); default "f32").

Value

A scan body fn(xs, margin) for scan_over().

Details

Hyperparameters are fixed scalars, fitted outside the raster pipeline (for example by marginal-likelihood MLE on sampled pixel series). Pixels with fewer than 3 valid observations return all-NaN. Initialisation is the large-variance diffuse approximation P1 = kappa * I.

See also

kalman_smooth(), scan_over(), g_scan()