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If you add an accelerometer to the project, a Kalman filter can give a good estimation of vertical speed. With only a barometric sensor, I don't think it's possible to reduce the lag below 1 second. import numpy as np import matplotlib.pyplot as plt import random from filterpy.kalman import KalmanFilter from filterpy.common import Q_discrete_white_noise ...


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That depends on how many of the accelerometer parameters (mostly drift and misalignment) you're trying to estimate. If the IMU and the 'extra' accelerometer were in perfect alignment (and if their statistics are Gaussian), then the optimal combination of their outputs would be a simple weighted sum: $\vec {\hat a} = k_1 \vec a_1 + k_2 \vec a_2$ where (...


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