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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 Model3.2.1 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 Images4.5 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; 5.6.6 Integrated Track Management; 5.7 Summary; References.

6 Poisson Scattering Field6.1 Hysteresis; 6.2 Poisson and Multinomial Equivalence; 6.3 Dynamic Non-homogeneous Poisson Mixture Model; 6.3.1 Point Measurement Data; 6.3.2 Image Measurement Data; 6.4 Examples; 6.4.1 Measurement Rate Estimation; 6.4.2 Average Power Estimation; 6.4.3 Track Initiation; 6.5 Clutter Mapping; 6.6 Target Life Cycle; 6.7 Summary; References; 7 Known Non-Gaussian Target Appearance; 7.1 Grid-Based Maximisation for Non-Gaussian Appearance; 7.2 Particle-Based Maximisation for Non-Gaussian Appearance; 7.3 Cell-Varying Point Spread Function.

7.4 Gaussian Mixture Appearance Approximation7.5 Simulated Examples; 7.5.1 Linear Gaussian Appearance; 7.5.2 Linear Non-Gaussian Appearance; 7.5.3 Crossing Non-Gaussian Scenario; 7.5.4 Diverging Target Scenario; 7.6 Summary; References; 8 Adaptive Appearance Models; 8.1 Deterministic Gaussian Appearance; 8.2 Stochastic Gaussian Appearance; 8.3 General Framework for Appearance Estimation; 8.4 Gaussian Mixture Appearance; 8.5 Bounding Box; 8.6 Dirichlet Appearance; 8.7 Appearance Library; 8.8 Correlated Kinematics and Appearance; 8.9 Simulated Examples; 8.9.1 Gaussian Targets.

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