Struct ParticleFilter 

pub struct ParticleFilter { /* private fields */ }

Particle Filter for non-Gaussian/nonlinear state estimation

Uses Monte Carlo methods to estimate the posterior distribution of states in nonlinear/non-Gaussian systems.

Implementations

impl ParticleFilter

pub fn new( num_particles: usize, initial_state: Array1<f32>, initial_std: f32, process_noise_std: f32, measurement_noise_std: f32, ) -> IoResult<Self>

Create a new particle filter

Arguments

• num_particles - Number of particles
• initial_state - Initial state estimate
• initial_std - Initial state uncertainty (standard deviation)
• process_noise_std - Process noise standard deviation
• measurement_noise_std - Measurement noise standard deviation

pub fn predict<F>(&mut self, transition_fn: F)

where F: Fn(&Array1<f32>) -> Array1<f32>,

Predict step with custom state transition function

Arguments

• transition_fn - Function that takes a state and returns predicted state

pub fn update<F>(&mut self, measurement: &Array1<f32>, measurement_fn: F)

where F: Fn(&Array1<f32>) -> Array1<f32>,

Update step with custom measurement function

Arguments

• measurement - Observed measurement
• measurement_fn - Function that converts state to measurement

pub fn mean_state(&self) -> Array1<f32>

Get mean state estimate

pub fn covariance(&self) -> Array2<f32>

Get weighted covariance of state estimate

Trait Implementations

impl Clone for ParticleFilter

fn clone(&self) -> ParticleFilter

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ParticleFilter

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.