Kalman Filters

An algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, Kalman filtering is also known as linear quadratic estimation (LQE), and it produces estimates of unknown variables that tend to be more accurate than those that are based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. This is accomplished by estimating a joint probability distribution over the variables for each timeframe. Rudolf E. Kálmán, who was a significant contributor to the development of the theory behind the filter, is honored with the naming of the device.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Kalman filter

Chapter 2: Weighted arithmetic mean

Chapter 3: Multivariate random variable

Chapter 4: Covariance

Chapter 5: Covariance matrix

Chapter 6: Expectation-maximization algorithm

Chapter 7: Minimum mean square error

Chapter 8: Recursive least squares filter

Chapter 9: Linear-quadratic-Gaussian control

Chapter 10: Extended Kalman filter

(II) Answering the public top questions about kalman filters.

(III) Real world examples for the usage of kalman filters in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of kalman filters.

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