common import Q_discrete_white_noise # 初始化无迹卡尔曼滤波器 dt = 0.1 # 时间步长 fx = lambda x, dt: x # 状态转移函数 hx = lambda x: x # 测量函数 ukf = UKF(dim_x=1, dim_z=1, dt=dt, fx=fx, hx=hx, points=2) ukf.R *= 10 # 测量噪声协方差 ukf.Q = Q_discrete_white...
from filterpy.common import Q_discrete_white_noise Q = Q_discrete_white_noise(dim=2, dt=1., var=2.35) # 得到一个二维的,1秒的,方差是2.35的白噪声矩阵 此外,我们的狗没有任何控制输入,所以B=0, u=0,当然,0也是他们的默认值。 总结:我们作为设计师,任务就是指定矩阵。 X,P:状态和协方差 F、...
简单模拟个白噪声过程 np.random.seed(1)# plot of discrete white noiserandser=np.random.normal(size=1000)tsplot(randser,lags=30) 我们可以看到过程是随机且在0附近波动。ACF和PACF显示没有明显的序列相关。要记住,由于是正态分布采样的结果,我们应该在自相关图中看到大约5%的显著性。最下面,QQ图和概率图...
kalman import KalmanFilter from filterpy.common import Q_discrete_white_noiseNow, create the filtermy_filter = KalmanFilter(dim_x=2, dim_z=1)Initialize the filter's matrices.my_filter.x = np.array([[2.], [0.]]) # initial state (location and velocity) my_filter.F = np.array([[1...
(location and velocity)my_filter.F=np.array([[1.,1.], [0.,1.]])# state transition matrixmy_filter.H=np.array([[1.,0.]])# Measurement functionmy_filter.P*=1000.# covariance matrixmy_filter.R=5# state uncertaintymy_filter.Q=Q_discrete_white_noise(dim=2,dt=0.1,var=0.1)# ...
frommonimportQ_discrete_white_noise defkalman_filter_soc(voltage,current,temperature): #初始化卡尔曼滤波器 kf=KalmanFilter(dim_x=2,dim_z=3) #状态向量[SOC,dSOC/dt] kf.x=np.array([0.5,0.0]) #状态转移矩阵 kf.F=np.array([[1.,0.1], ...
# plot of discrete white noise randser = np.random.normal(size=1000) tsplot(randser, lags=30) 1. 2. 3. 4. 5. 从上图中可以看到: 这个过程是随机的,并且是围绕的中心是零。 自相关(autocorrelation, ACF)和偏自相关(partial autocorrelation, PACF)也没有表现出明显的序列相关。由于是正态分布采样...
Q = np.eye(2) Q[0,0] = Q[0,0]*sigmaQv[0]**2 # pertubation from white noise, may be from Q[1,1] = Q[1,1]*sigmaQv[1]**2 sigmaRm = 5 # a priori precision of measurement R = np.array([[sigmaRm**2]]) # covariance of measurement precision ...
(being white noise) affects the signal at all frequencies. Due to the normalization that occurs when calculating coherence at each frequency component (which removes information about amplitude), the presence of white noise at many frequencies ends up being dominant in the final averaged coherence ...
noise_type == 'white': n = np.random.rand(signal.shape[0]) noise = ((n - n.mean())/n.std())*var elif noise_type == 'pink': n = cn.powerlaw_psd_gaussian(1, signal.shape[0]) noise = ((n - n.mean())/n.std())*var elif noise_type == '0.1 inc white': n = np...