dwsmith said:In this presentation, on page 7, they say due to noise \(y_1 = 0.9\). How or where did they get this value?
It isn't an article just a beamer presentation so going from page 1 - 7 is quick and easy.
A Kalman filter is a mathematical algorithm used for state estimation in systems that are subject to noise and uncertainty. It is commonly used in fields such as control systems, signal processing, and navigation.
A Kalman filter uses a series of measurements and a mathematical model to estimate the true state of a system. It combines the noisy measurements with the model predictions to improve the accuracy of the estimate.
The y1 value, or observation vector, is a measurement of the system's state taken from a sensor. It is used in the Kalman filter algorithm to update the state estimate and reduce the effects of noise and uncertainty.
The y1 value is used in the Kalman filter to correct the state estimate based on the most recent measurement. It is essential for accurately estimating the true state of the system, especially in the presence of noise.
Yes, a Kalman filter can work with multiple observation vectors, each representing a different measurement of the system's state. This allows for a more comprehensive and accurate estimation of the true state, as well as the ability to handle multiple sources of noise and uncertainty.