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Overview
Get the solutions to your most challenging tracking problems with this up-to-date resource. Using the Bayesian inference framework, the book helps you design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. The book shows you how non-linear Multiple Hypothesis Tracking and the Theory of Unified Tracking are successful methods when multiple target tracking must be performed without contacts or association.With detailed examples illustrating the developed concepts, algorithms, and approaches — the book helps you:
>Track when observations are non-linear functions of target site, when the target state distributions or measurement error distributions are not Gaussian, in low data rate and low signal to noise ratio situations, and when notions of contact and association are merged or unresolved among more than one target
>Detect and track when a single sensor response is not strong enough to call a contact
>Determine bounds on tracker performance from the characteristics of the targets and sensors
>Set optimal threshold levels for calling contacts in likelihood ratio detection and tracking, and compute association probabilities of joint observations and non-geometric information
"...helps in the design and development of mathematically sound algorithms for dealing with tracking problems that involve multiple targets, multiple sensors, and multiple platforms...includes detailed examples."
Synopsis
Get the solutions to your most challenging tracking problems with this up-to-date resource. Using the Bayesian inference framework, the book helps you design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. The book shows you how non-linear Multiple Hypothesis Tracking and the Theory of Unified Tracking are successful methods when multiple target tracking must be performed without contacts or association.
Booknews
Two mathematicians and an engineer with a consulting firm trace their experience with tracking to the CASP search and rescue planning program they developed for the Coast Guard in the 1970s, applying a stochastic model to predict the target's position and motion over time. While most books on tracking present algorithms for problems in which contacts are received at a high data rate with good localization information about the targets, the focus here is on low data rate, low signal-to-noise ratio situations where sensor responses provide ambiguous information about the target's state. They explain Bayesian inference as a statistical decision theory framework from which to view and design tracking algorithms. Includes an appendix on Gaussian density lemma. Annotation c. Book News, Inc., Portland, OR (booknews.com)