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Introduction to Random Signals and Applied Kalman Filtering: with Matlab Exercises

Introduction to Random Signals and Applied Kalman Filtering: with Matlab Exercises, you will download this book in this post.

Advances in computers and personal navigation systems have greatly expanded the applications of Kalman filters. A Kalman filter uses information about noise and system dynamics to reduce uncertainty from noisy measurements. Common applications of Kalman filters include such fast-growing fields as autopilot systems, battery state of charge (SoC) estimation, brain-computer interface, dynamic positioning, inertial guidance systems, radar tracking, and satellite navigation systems.

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Table of Contents
PART 1: RANDOM SIGNALS BACKGROUND
  • Probability and Random Variables: A Review
  • Mathematical Description of Random Signals
  • Linear Systems Response, State-space Modeling and Monte Carlo Simulation
PART 2: KALMAN FILTERING AND APPLICATIONS
  • Discrete Kalman Filter Basics
  • Intermediate Topics on Kalman Filtering
  • Smoothing and Further Intermediate Topics
  • Linearization, Nonlinear Filtering and Sampling Bayesian Filters
  • The "Go-Free" Concept, Complementary Filter and Aided Inertial Examples
  • Kalman Filter Applications to the GPS and Other Navigation Systems

Download Introduction to Random Signals and Applied Kalman Filtering: with Matlab Exercises PDF

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