Square-Root Unscented Kalman Filter. More...

#include <srukf.h>

Public Attributes
srukf_workspace *	ws

State Estimate
The current best estimate of the system state
srukf_mat *	x

srukf_mat *	S

Noise Covariances
Process and measurement noise, stored as square-roots
srukf_mat *	Qsqrt

srukf_mat *	Rsqrt

UKF Parameters
Tuning parameters controlling sigma point distribution
srukf_value	alpha

srukf_value	beta

srukf_value	kappa

srukf_value	lambda

Sigma Point Weights
Weights for reconstructing mean and covariance from sigma points
srukf_value *	wm

srukf_value *	wc

Detailed Description

Square-Root Unscented Kalman Filter.

This structure holds the complete state of an SR-UKF instance:

Current state estimate and its sqrt-covariance
Noise covariances (as square-roots)
UKF tuning parameters and derived weights
Pre-allocated workspace for efficiency

Key insight: We store \(S\) where \(P = SS^T\), not \(P\) itself. This is the "square-root" in SR-UKF, providing numerical stability.

See also: srukf_create, srukf_create_from_noise

Definition at line 391 of file srukf.h.

Member Data Documentation

◆ alpha

srukf_value srukf::alpha

Spread of sigma points (typically 1e-3 to 1). Smaller values keep points closer to the mean.

Definition at line 417 of file srukf.h.

Referenced by srukf_compute_weights(), and srukf_set_scale().

◆ beta

srukf_value srukf::beta

Prior distribution knowledge (2.0 for Gaussian). Affects the zeroth covariance weight.

Definition at line 419 of file srukf.h.

Referenced by srukf_compute_weights(), and srukf_set_scale().

◆ kappa

srukf_value srukf::kappa

Secondary scaling (typically 0 or 3-N). Can be used to ensure semi-positive definiteness.

Definition at line 421 of file srukf.h.

Referenced by srukf_set_scale().

◆ lambda

srukf_value srukf::lambda

Computed: \(\alpha^2 (N + \kappa) - N\). Determines actual sigma point spread.

Definition at line 423 of file srukf.h.

Referenced by srukf_compute_weights(), srukf_correct_core(), srukf_predict_core(), and srukf_set_scale().

◆ Qsqrt

srukf_mat* srukf::Qsqrt

Sqrt of process noise covariance (N x N). Models uncertainty in the state transition. Larger values = less trust in the model.

Definition at line 405 of file srukf.h.

Referenced by srukf_free(), srukf_predict_core(), and srukf_set_noise().

◆ Rsqrt

srukf_mat* srukf::Rsqrt

Sqrt of measurement noise covariance (M x M). Models sensor noise. Larger values = less trust in measurements.

Definition at line 408 of file srukf.h.

Referenced by srukf_correct_core(), srukf_free(), srukf_meas_dim(), and srukf_set_noise().

◆ S

srukf_mat* srukf::S

Sqrt of state covariance (N x N, lower triangular). Satisfies \(P = SS^T\) where P is the covariance.

Definition at line 397 of file srukf.h.

Referenced by srukf_correct(), srukf_free(), srukf_get_sqrt_cov(), srukf_predict(), srukf_reset(), and srukf_set_sqrt_cov().

◆ wc

srukf_value* srukf::wc

Covariance weights (2N+1 elements). \(w^c_0 = w^m_0 + (1 - \alpha^2 + \beta)\), \(w^c_i = w^m_i\) for i>0. Note: \(w^c_0\) can be negative for small alpha!

Definition at line 434 of file srukf.h.

Referenced by srukf_compute_weights(), srukf_correct_core(), srukf_free(), and srukf_predict_core().