Download Bayesian Multiple Target Tracking by Lawrence D Stone PDF

By Lawrence D Stone

This moment version has passed through tremendous revision from the 1999 first version, spotting lot has replaced within the a number of goal monitoring box. some of the most dramatic alterations is within the frequent use of particle filters to enforce nonlinear, non-Gaussian Bayesian trackers. This publication perspectives a number of aim monitoring as a Bayesian inference challenge. inside this framework it develops the speculation of unmarried goal monitoring. as well as supplying a close description of a uncomplicated particle clear out that implements the Bayesian unmarried aim recursion, this source offers a variety of examples that contain using particle filters.

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Extra resources for Bayesian Multiple Target Tracking

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To do this, he must analyze statistical procedures with respect to all events that might occur in the experiment. The conditionalist focuses on the events that actually occurred in the experiment. He is interested in the evaluation of the likelihood function just for the events that have actually occurred. 1 Likelihood Principle In this book we adopt a specific form of the conditionality principle called the likelihood principle. The likelihood principle states that the likelihood function evaluated at the observed events is a complete summary of the information in the observations.

We can substitute any motion model that we can simulate for the one used above. We can incorporate measurements from any sensor for which we can calculate a likelihood function. We can incorporate information from multiple sensors, colocated or not, and the sensors can be disparate producing different types of measurements. 8 we see that the initial bearing produces a probability distribution on target location with substantial range uncertainty. 5 nm from ownship at the time of the initial bearing.

In order to obtain a good statistical representation of the joint four-dimensional position-velocity distribution, a large number of particles is required. On the other hand 25,000 particles present a modest challenge for today’s computers. 5, we use 10,000 particles per target. The smaller number is satisfactory in this case because the measurement information, position estimates with bivariate normal errors, provides both range and bearing information. 2. 5 nm from ownship, we started the particle filter at the time of the first measurement by drawing 25,000 positions from this distribution and then drawing the velocity for each particle from the initial velocity distribution.

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