ABSTRACT

System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notation

chapter 1|22 pages

Signal-Plus-Noise Models

chapter 2|30 pages

The Fundamental Covariance Structure

chapter 3|14 pages

Recursions for L and L−1

chapter 4|22 pages

Forward Recursions

chapter 5|18 pages

Smoothing

chapter 6|30 pages

Initialization

chapter 7|16 pages

Normal Priors

chapter 8|16 pages

A General State-Space Model