Bayesian Estimation and Tracking: A Practical Guide

1.3 Scope of this Text

1.3.1 Objective

The objective of this book is to give the reader a firm understanding of Bayesian estimation methods and their interrelatedness. Starting with the first principles of Bayesian theory, we show how each tracking filter is derived from a slight modification to a previous filter. Such a development gives the reader a broader understanding of the hierarchy of Bayesian estimation and tracking. Following the discussions about each tracking filter, the filter is put into both pseudo-code and process flow block diagram form for ease in future recall and reference.

In his seminal book on filtering theory [3], originally published in 1970, Jazwinski stated that “The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been collected in a single source. Second, available literature on the continuous nonlinear theory is quite esoteric and controversial, and thus inaccessible to engineers uninitiated in measure theory and stochastic differential equations.” A similar statement can be made about the current state of affairs in non-Gaussian Monte Carlo methods of estimation theory. Most of the published work is esoteric and inaccessible to engineers uninitiated in measure theory. The edited book of invited papers by Doucet et al. [4] is a prime example. This is an excellent book of invited papers, but is extremely esoteric in many of its stand-alone ...