Track-Before-Detect Using Expectation Maximisation : The Histogram Probabilistic Multi-hypothesis Tracker - Info and Reading Options

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Intro -- Foreword -- Reference -- Acknowledgements -- Contents -- About the Authors -- Parlance -- 1 Introduction -- 1.1 Historical Development of H-PMHT -- 1.2 Preliminaries -- 1.3 Expectation--Maximisation -- 1.4 Notation -- 1.5 Canonical Multi-target Scenario -- 1.6 Measures of Performance -- 1.6.1 Cardinality Measures -- 1.6.2 Association Measures -- 1.6.3 Accuracy Measures -- 1.6.4 Track to Truth Association -- 1.7 Monograph Synopsis -- References -- 2 Idealised Track-Before-Detect -- 2.1 Single Target Comparison -- 2.2 Summary -- References -- 3 Point Measurement Probabilistic Multi-hypothesis Tracking -- 3.1 Gaussian Mixture Models -- 3.2 Dynamic Mixture Model -- 3.2.1 Expectation Step -- 3.2.2 Linear Gaussian Maximisation Step -- 3.3 Non-Gaussian Mixtures -- 3.4 Incorporating Clutter -- 3.5 Examples of PMHT Point Measurement Tracking -- 3.5.1 Two Targets -- 3.5.2 Numerous Targets -- 3.6 Problems with PMHT -- 3.6.1 Model Order Estimation -- 3.6.2 Adaptivity -- 3.6.3 Optimism -- 3.7 Summary -- References -- 4 Histogram Probabilistic Multi-hypothesis Tracking -- 4.1 Histogram Data Association -- 4.1.1 Expectation Step -- 4.1.2 Maximisation Step -- 4.2 Unobserved Pixels -- 4.3 Image Quantising -- 4.3.1 Quantisation in the Limit -- 4.3.2 Resampled Target Prior -- 4.4 Associated Images -- 4.5 Algorithm Summary for Gaussian Appearance -- 4.6 Simulated Example -- 4.7 Summary -- References -- 5 Implementation Considerations -- 5.1 Alternative Resampled Prior -- 5.2 Integrals -- 5.3 Vectorised Two-Dimensional Case -- 5.4 Single-Target Chip Processing -- 5.5 Covariance Estimates -- 5.5.1 Observed Information -- 5.5.2 Joint Probabilistic Data Association -- 5.6 Track Management -- 5.6.1 Track Quality Score -- 5.6.2 Hierarchical Track Update -- 5.6.3 Track Decisions -- 5.6.4 Image Vetting -- 5.6.5 New Candidate Formation

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