Exploring the relationship between Kalman filtering and optimal frequency domain filtering.
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How the Kalman filter uses its knowledge of a system's dynamics to predict its next move, and why that derives from Bayesian probability rules
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2
Why systems containing positive feedback loops will always be more prone to bouncing, wobbling, oscillating, jumping, vibrating, overshooting, and…
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In which we explore how the Kalman filter approximates Bayesian probability in its update process, and then push it beyond its limits as we tackle the…
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A deep-dive
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From time to time, I’ll post and comment on excerpts from the works of E. T. Jaynes that are particularly interesting or insightful. Here’s one that…
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A deep-dive
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"I need to know about what now?!" Right-half-plane zeros. Or at least, I want you to know about them, because there's a really critical one that's about…
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These Are Systems