Probability and Random Variables: A Review Mathematical Description of Random Signals Response of linear Systems to Random Inputs Wiener Filtering The Discrete Kalman Filter, State-Space Modeling, and Simulation

Organized for use as a text for an introductory course in stochastic processes at the senior level and as a first-year, graduate-level course in Kalman filtering theory and applications, this book includes real-world problems in practice as illustrative examples, and also covers the more practical aspects of implementation. The author Grewal teaches at Cal State Fullerton and also offers seminars and tutorials on Kalman Filters. Dr. Grewal has contributed the Article on Kalman Filters for the Webster Encyclopedia

卡尔曼滤波经典书籍，Introduction To Random Signals And Applied Kalman Filtering，英文原版第3版

Kalman Filtering 经典著作，大牛Paul Zarchan。附源代码！！！

Kalman Filtering and Neural Networks This self-contained book, consisting of seven chapters, is devoted to Kalman filter theory applied to the training and use of neural networks,and some applications of learning algorithms derived in this way.

Introduction to Random Signals and Applied Kalman Filtering,4版代码，好东西 学习信号处理和卡尔曼滤波经典教材的代码

Robert Grover Brown , Patrick Y. C. Hwang 经典的卡尔曼滤波书籍，清晰第4版。 In this updated edition the main thrust is on applied Kalman filtering. Chapters 1-3 provide a minimal background in random process theory and the response of linear systems to random inputs. The following chapter is devoted to Wiener filtering and the remainder of the text deals with various facets of Kalman filtering with emphasis on applications. Starred problems at the end of each chapter are computer exercises. The authors believe that programming the equations and analyzing the results of specific examples is the best way to obtain the insight that is essential in engineering work.

This is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB®, and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce mistakes to the initial filter designs to show the reader what happens when the filter is not working properly. The text carefully sets up a problem before the Kalman filter is actually formulated, to give the reader an intuitive feel for the problem being addressed. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches. Readers will gain experience in software and performance tradeoffs for determining the best filtering approach. The material that has been added to this edition is in response to questions and feedback from readers. The third edition has three new chapters on unusual topics related to Kalman filtering and other filtering techniques based on the method of least squares.Chapter 17 presents a type of filter known as the fixed or finite memory filter, which only remembers a finite number of measurements from the past. Chapter 18 shows how the chain rule from calculus can be used for filter initialization or to avoid filtering altogether. A realistic three-dimensional GPS example is used to illustrate the chain-rule method for filter initialization. Finally, Chapter 19 shows how a bank of linear sine-wave Kalman filters, each one tuned to a different sine-wave frequency, can be used to estimate the actual frequency of noisy sinusoidal measurements and obtain estimates of the states of the sine wave when the measurement noise is low.

卡尔曼滤波比较好的一本书，

卡尔曼滤波入门的经典教材