Hardback : £150.00
Using the Bayesian inference framework, this book enables the reader to design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. It shows how non-linear Multiple Hypothesis Tracking and the Theory of United 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 the reader track when observations are non-linear functions of target site, when the target state distributions or measurements error distributions are not Gaussian, when notions of contact and association are merged or unresolved among more than one target, and in low data rate and low signal to noise ratio situations.
Using the Bayesian inference framework, this book enables the reader to design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. It shows how non-linear Multiple Hypothesis Tracking and the Theory of United 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 the reader track when observations are non-linear functions of target site, when the target state distributions or measurements error distributions are not Gaussian, when notions of contact and association are merged or unresolved among more than one target, and in low data rate and low signal to noise ratio situations.
The Multiple Target Detection and Tracking Problem -- The Case for the Bayesian Inference. Single Target Tracking -- Bayesian Filtering. Kalman Filtering. Discrete Bayesian Filtering. Classical Multiple Target Tracking -- General Multiple Hypothesis Tracking. Classical Multiple Hypothesis Tracking. Multiple Target Tracking Without Contacts or Association -- General Multiple Target Model. Relationship to Multiple Hypothesis Tracking. Likelihood Ratio Detection and Tracking: Theoretical Foundations. Likelihood Ratio Detection and Tracking: Implementation Issues. Appendices.
Lawrence D. Stone is Chief Operating Officer at Metron, Inc. He received his Ph.D. and MS in mathematics from Purdue University. Carl A. Barlow is an independent scientific consultant. He holds S.B. and S.M. degrees in theoretical physics from MIT. Thomas L. Corwin is Chief Executive Officer of Metron, Inc. He received his Ph.D and MS in statistics from Princeton University.
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