粒子滤波代码 SIR

Sure! Here's an example code for Sequential Importance Resampling (SIR) particle filter in Python:

import numpy as np

def motion_model(x, dt):
    # Add motion model here
    # Returns the predicted state based on the current state and time step

def measurement_model(x):
    # Add measurement model here
    # Returns the likelihood of observing a measurement given the current state

def resample(particles, weights):
    indices = np.random.choice(range(len(particles)), size=len(particles), replace=True, p=weights)
    return particles[indices]

def particle_filter(z, particles, weights):
    N = len(particles)
    resampled_particles = np.zeros_like(particles)

    for i in range(N):
        # Particle prediction
        particles[i] = motion_model(particles[i], dt)

        # Particle weight update
        weights[i] = measurement_model(particles[i])
    
    # Normalize weights
    weights /= np.sum(weights)

    # Resampling
    resampled_particles = resample(particles, weights)

    return resampled_particles

# Initialize particles and weights
N = 100  # Number of particles
particles = np.random.uniform(low=0, high=10, size=N)
weights = np.ones(N) / N

# Simulate measurements
T = 10  # Number of time steps
dt = 0.1  # Time step size
measurements = np.random.uniform(low=0, high=10, size=T)

# Run particle filter
for t in range(T):
    particles = particle_filter(measurements[t], particles, weights)

# Estimated state
estimated_state = np.mean(particles)
print("Estimated state:", estimated_state)

Note that this is a basic implementation and may need modification based on your specific problem and models. Make sure to define your motion model and measurement model functions accordingly.