MHB Kalman filter where does y_1 come from?

AI Thread Summary
The value \(y_1 = 0.9\) in the presentation is a hypothetical measurement derived from sensor data, reflecting noise in the system. It represents the first observation of the float level, denoted as \(y_i\) in the context of the Kalman filter. The discussion clarifies that this value is not based on empirical data but rather a conceptual example. The authors likely used this figure to illustrate the impact of noise on measurements. Understanding this context is crucial for interpreting the Kalman filter's application in the presentation.
Dustinsfl
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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.
 
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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.

It came out of the authors head, it is a hypothetical measurement that you might have gotten from the sensor. The 's are the measurements (section 3 first sentence is the level of the float). So is the th observation (measurement) of the float level.

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