Continuous Polynomial Kalman Filter. Illustrating the relationship between continuous and discrete Kalman filters.
Examples of how continuous filters can be used to help understand discrete filters through such concepts as transfer function and bandwidth. Extended Kalman Filtering. How to apply extended Kalman filtering and Riccati equations to a practical nonlinear problem in tracking. Showing what can go wrong with several different design approaches and how to get designs to work.
Why choice of states can be important in a nonlinear filtering problem.
Drag and Falling Object. Designing two different extended filters for this problem. Cannon Launched Projectile Tracking Problem.
Fundamentals of Kalman Filtering_A Practical Approach
Developing extended filters in the Cartesian and polar coordinate systems and comparing performance. Showing why one must not always pay attention to the academic literature. Comparing extended and linear Kalman filters in terms of performance and robustness. Tracking a Sine Wave.
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Developing three different extended Kalman filter formulations and comparing performance of each in terms of robustness. Determining receiver location based on range measurements to several satellites. Showing how receiver location can be determined without any filtering at all. How satellite spacing influences performance.
Illustration of filter performance for both stationary and moving receivers. Filtering techniques for estimating biases in a satellite navigation problem. A second new chapter presents techniques for improving Kalman filter performance. Included is a practical method for preprocessing measurement data when there are too many measurements for the filter to utilize in a given amount of time.
The chapter also contains practical methods for making the Kalman filter adaptive. A new appendix has been added which serves as a central location and summary for the text's most important concepts and formulas. Read more Read less. No customer reviews.
Share your thoughts with other customers. Write a customer review. Because real problems are seldom presented as differential equations, and usually do not have unique solutions, the authors illustrate several different filtering approaches.
Kalman Fundamentals - Build both linear and extended Kalman filters
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.
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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. Skip to main content.