原文排版更清爽:g-h 滤波器 与 PID 控制 (Filter & Control) 之前的一篇文章介绍了 互补滤波器 (Complementary Filter),并演示了如何从 IMU 原始数据,用互补滤波器得到更准确的 加速度 和 旋转角速度 (核心算法的代码就 7 行)。 在理解了最简单的 互补滤波器后, 就可以进一步了解稍稍高级一点的 g−h 滤...
data = g_h_filter(data=zs, x0=10., dx=0., g=0.2, h=0.02) 从图中可以看到,每一个滤波器输出的估计值都滞后真实值。这是合理的,因为我们在建立过程模型的时候假设时匀速的,此种情况我们很难通过调整滤波器的参数改善性能。This is called the lag error or systemic error of the system.It is a...
classRobot:def__init__(self,state=0,speed=0):self.state=stateself.speed=speed# 机器人的实际位置self.actual_locations=[state]# 为了简化模型,这是一个只会做匀加速运动的机器人# 但其实gh-filter不需要是匀加速运动,根本不care你是什么运动defrun(self,acc=0,t=0):ift<=0:returnforiinrange(t):...
estimates, predictions = [estimated_weight], [] # most filter literature uses 'z' for measurements for z in weights: # predict new position predicted_weight = estimated_weight + gain_rate * time_step # update filter estimated_weight = predicted_weight + scale_factor * (z - predicted_weight...
This algorithm is known as theg-h filteror theαα-ββ filter.gg andhh refer to the two scaling factors that we used in our example.gg is the scaling we used for the measurement (weight in our example), andhh is the scaling for the change in measurement over time (lbs/day in our...
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoothers, and more. Has companion book 'Kalman and Bayesian
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