Probability & Statistics of Noisy Signals for Kalman Filters, Guidance Fundamentals II, Section 1.2
In this lesson, we develop fundamental probability and statistical concepts for working with noisy signals in stochastic control and Kalman filter design. Topics include: noisy signal characterization, sample space, mean, expected value, variance, stationary processes, covariance, the covariance matrix, the joint moment matrix, the autocorrelation matrix, uniform distributions, and gaussian distributions.
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