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IEEE/ICACT20240248 Slide.11        [Big Slide]	Oral Presentation
	These are Kalman Filter equations. In those equations, we can focus on the covariance; this is a measure of the estimated uncertainty of the prediction state. Next, we can look at the Kalman Gain; the Kalman gain is the weight given to the measurements and predictions. We use Kalman gain to determine which is more reliable. With a high gain, the filter places more weight on the most recent measurements and thus conforms to them. With a low gain, the filter conforms to the predictions more closely.

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