Bayesian Estimation and Tracking: A Practical Guide by Anton J. Haug

By Anton J. Haug

A sensible method of estimating and monitoring dynamic platforms in real-worl applications

Much of the literature on appearing estimation for non-Gaussian structures is brief on functional technique, whereas Gaussian equipment usually lack a cohesive derivation. Bayesian Estimation and Tracking addresses the space within the box on either bills, delivering readers with a finished assessment of tools for estimating either linear and nonlinear dynamic structures pushed through Gaussian and non-Gaussian noices.

Featuring a unified method of Bayesian estimation and monitoring, the ebook emphasizes the derivation of all monitoring algorithms inside a Bayesian framework and describes potent numerical tools for comparing density-weighted integrals, together with linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian situations. the writer first emphasizes designated derivations from first ideas of eeach estimation technique and is going directly to use illustrative and targeted step by step directions for every process that makes coding of the monitoring clear out basic and straightforward to understand.

Case reports are hired to exhibit functions of the mentioned subject matters. moreover, the ebook offers block diagrams for every set of rules, permitting readers to advance their very own MATLAB® toolbox of estimation methods.

Bayesian Estimation and Tracking is a wonderful e-book for classes on estimation and monitoring tools on the graduate point. The booklet additionally serves as a useful reference for learn scientists, mathematicians, and engineers looking a deeper realizing of the topics.

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We begin with a discussion of a general polynomial expansion of a scalar function. This leads directly to the derivation of a scalar Taylor polynomial approximation for a nonlinear function. A numerical approximation of the Taylor polynomial is then derived by replacing the differentials of the Taylor polynomial by their finite difference equivalents, resulting in Stirling’s interpolation formula (Stirling’s polynomial). This is followed by a subsection where the scalar approximations are generalized to approximating multidimensional nonlinear functions with multidimensional arguments through the introduction of multidimensional polynomial expansions.

Declare the posterior belief our new prior belief and return to 2. This concept was first formalized in a paper by the Reverend Thomas Bayes, read to the Royal Statistical Society in 1763 by Richard Price several years after Bayes’ death. An excellent review of the history and concepts associated with Bayesian statistical inference can be found in the paper by Stephen Brooks [2]. Brooks’ paper also has some interesting examples that contrast the Bayesian method with the so-called “Frequentist” method for statistical inference.

2) with commas separating the elements of the vector. [·] represent a vector or matrix transpose, which will be defined below. An n × m matrix is a two-dimensional array of the form ⎡ ⎤ a11 · · · a1m ⎢ . ⎥ .. 3) . ⎦ ⎣ .. an1 · · · anm The first dimension is the number of rows and the second is the number of columns. The ijth component of the matrix A is aij . The transpose of the matrix A is designated as A and is defined by ⎤ ⎡ a11 · · · an1 ⎢ . ⎥ .. 4) . ⎦ ⎣ .. 5) i=1 Equivalently, the trace of a matrix is the sum of its eigenvalues, making it an invariant with respect to a change of basis.

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