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Information Technology

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This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based  on the Monte Carlo statistical method. Although the resulting  algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

Particle Filters for Random Set Models

POST SECONDARY FOUR YEAR

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PARTICLE FILTERS FOR RANDOM SET MODELS

Duration

Duration Unit Of Measure

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Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Preface.
Contents.
Acronyms.
Notations.
1: Introduction.
2: Background.
3: Applications Involving Non-standard Measurements.
4: Multi-Object Particle Filters.
5: Sensor Control for Random Set Based Particle Filters.
6: Multi-Target Tracking.
7: Advanced Topics.
Index.

Particle Filters for Random Set Models.css-1p8rvzy{position:absolute;padding-left:8px;}

Particle Filters for Random Set Models