Podcast #128: We chat with Kent C Dodds about why he loves React and discuss what life was like in the dark days before Git. Listen now.
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KF is actually a mixture of a deterministic state propagator and a statistical estimator. Despite it's name including the term filter, Kalman filter is not a simple frequency selective one. It's indeed a statistical recursive estimator of a state of a (linear) dynamic system. Yet on a broader sense it's called as a filter as it will separate a desired ...


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Perhaps an analogy might be constructive. Consider a submarine commander with a fat tanker in the cross hairs of his periscope. He needs to shoot his torpedoes, not at where the target is now, but at some place where the torpedo will intersect with the target. A skilled commander will have knowledge about how fast or slow the tanker can go. knowledge ...


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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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Simple Description Imagine you're in a car that is traveling at 70MPH with cruise control. Because the cruise control isn't perfect, your actual speed might vary slightly. This imperfection is called "process noise". Now lets also imagine the car is being tracked using GPS. Because GPS isn't perfect, there will be some noise in the sensor reading. This ...


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See: What is the relationship between a Kalman filter and polynomial regression? In over-simplified form, eyeball a line though a cloud of data samples, look where that line might point one sample into the future; and, when you get a new sample check, how good that estimate might have been; then redo, but optimize for a lot less arithmetic per step. A ...


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