python - 如何在单次观察中运行 pykalman 卡尔曼滤波器？ (Python)

Question

我可以运行 pykalman documentation 中给出的简单 pykalman 卡尔曼滤波器示例:

import pykalman
import numpy as np
kf = pykalman.KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
measurements = np.asarray([[1,0], [0,0], [0,1]])  # 3 observations
(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)
print filtered_state_means

这会正确返回状态估计(每个观察一个):

[[ 0.07285974  0.39708561]
 [ 0.30309693  0.2328318 ]
 [-0.5533711  -0.0415223 ]]

但是，如果我只提供一个观察结果，代码就会失败:

import pykalman
import numpy as np
kf = pykalman.KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
measurements = np.asarray([[1,0]])  # 1 observation
(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)
print filtered_state_means

错误如下:

ValueError: could not broadcast input array from shape (2,2) into shape (2,1)

我如何使用 pykalman 仅通过一次观察来更新初始状态和初始协方差？

Answer 1

来自文档:http://pykalman.github.io/#kalmanfilter

filter_update(filtered_state_mean, filtered_state_covariance, observation=None, transition_matrix=None, transition_offset=None, transition_covariance=None, observation_matrix=None, observation_offset=None, observation_covariance=None)

这接受时间 t 的 filtered_state_mean 和 filtered_state_covariance，以及 t+1 的观察值，并返回 t+1 的状态均值和状态协方差(用于下一次更新)