By Anton J. Haug
A functional method of estimating and monitoring dynamic structures in real-worl applications
Much of the literature on acting estimation for non-Gaussian structures is brief on useful method, whereas Gaussian equipment usually lack a cohesive derivation. Bayesian Estimation and Tracking addresses the distance within the box on either bills, delivering readers with a entire assessment of equipment for estimating either linear and nonlinear dynamic platforms pushed through Gaussian and non-Gaussian noices.
Featuring a unified method of Bayesian estimation and monitoring, the e-book emphasizes the derivation of all monitoring algorithms inside a Bayesian framework and describes powerful numerical equipment for comparing density-weighted integrals, together with linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian circumstances. the writer first emphasizes specific derivations from first rules of eeach estimation strategy and is going directly to use illustrative and precise step by step directions for every process that makes coding of the monitoring clear out easy and simple to understand.
Case experiences are hired to show off purposes of the mentioned issues. moreover, the e-book offers block diagrams for every set of rules, permitting readers to increase their very own MATLAB® toolbox of estimation methods.
Bayesian Estimation and Tracking is a wonderful e-book for classes on estimation and monitoring equipment on the graduate point. The ebook additionally serves as a useful reference for examine scientists, mathematicians, and engineers looking a deeper figuring out of the topics.
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Additional resources for Bayesian Estimation and Tracking: A Practical Guide
See a text on matrix linear algebra for the particulars of each technique. 2 Within MATLAB® , there are two predominant methods to find the solution of the equation A ∗ x = b, one of which is preferred. The less favorable, but most straightforward method is to take x = A−1 ∗b literally: x = inv(A)*b. Here, inv(A) is a MATLAB® function that computes A−1 analytically and the operator * represents matrix multiplication. The preferable solution is found using the matrix left division operator or backward slash: x = A\b.
Suppose now, that one has a small portable radar on hand. The radar can be used to measure (observe) the line-of-sight range and range rate of the ship to within some measure of uncertainty. Given the right mathematical model, one that links the observations to the Cartesian coordinates of the ships position and velocity, a current radar measurement can be used to update the predicted ships state (position and velocity). 5 BAYESIAN HIERARCHY OF ESTIMATION METHODS The above paragraph contains the essence of recursive Bayesian estimation: 1.
Thus, this book is all about numerical methods for evaluating density-weighted integrals for both Gaussian and non-Gaussian densities. For each estimation method that we discuss and derive, we present both pseudocode and graphic block diagram that can be used as tools in developing a softwarecoded tracking toolbox. As an aid in understanding the methods presented, we also discuss what is required to develop simulations for several very specific real-world problems. These case-study problems will be addressed in great detail, with track estimation results presented for each.
Bayesian Estimation and Tracking: A Practical Guide by Anton J. Haug