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Preface | p. xi |

Introduction | p. xiii |

Design of a Tracking Algorithm for an Advanced ATC System | p. 1 |

Introduction | p. 1 |

The Hadamard Project | p. 1 |

Characteristics of the New ATC System | p. 2 |

Tracking Accuracy Requirements | p. 3 |

Aircraft Motion Modeling | p. 5 |

Modeling Influence on Estimation Quality | p. 5 |

Aircraft Maneuver Modeling | p. 7 |

Tracking Algorithm | p. 10 |

Multiple Model Formulation of Aircraft Trajectory | p. 10 |

The Interacting Multiple Model Algorithm | p. 14 |

Evaluation of the IMM for Air Traffic Simulations | p. 16 |

Algorithms Based on Exact Maneuver Modeling | p. 17 |

Algorithms Based on Approximate Maneuver Modeling | p. 21 |

Algorithm Selection for Hadamard Tracking System | p. 26 |

Conclusion | p. 28 |

References | p. 28 |

Design of a Multisensor Tracking System for Advanced Air Traffic Control | p. 31 |

Introduction | p. 31 |

Multisensor Tracking Modules | p. 32 |

Coordinate Transformation | p. 33 |

Track Maintenance (Continuation) | p. 33 |

Track Deletion | p. 33 |

Measurement Memorization | p. 34 |

Track Formation (Initiation) | p. 34 |

Track Merging | p. 34 |

Systematic Error Estimation | p. 34 |

Aircraft Track Selection | p. 35 |

Synchronization | p. 35 |

Bayesian Track Continuation | p. 35 |

Systematic Error Estimation | p. 38 |

Evaluation of the Tracking Performance | p. 41 |

Summary and Conclusions | p. 48 |

Jumpdif Track Maintenance Equations in the Horizontal Direction | p. 48 |

Interaction Step of Generalized IMM | p. 49 |

EKF Time Extrapolation Equations | p. 51 |

PDA-Based Measurement Update Equations | p. 53 |

Output Calculations | p. 56 |

Joint Tracking and Sensors' Systematic Error Estimation | p. 56 |

Extended Kalman Filter | p. 58 |

The Single-Sensor Situation | p. 59 |

The Multisensor Situation | p. 59 |

Systematic Error Estimation After Convergence | p. 60 |

References | p. 62 |

Passive Sensor Data Fusion and Maneuvering Target Tracking | p. 65 |

Introduction | p. 65 |

The Application: Passive Sensor Data Fusion | p. 66 |

A Hybrid Model Based Algorithm: The IMMPDA Filter | p. 69 |

Hybrid Systems | p. 69 |

Hybrid Filters | p. 70 |

Target Motion Models | p. 75 |

First Set of Models | p. 75 |

Second Set of Models | p. 76 |

Third Set of Models | p. 78 |

Simulation Results | p. 79 |

Parameter Values | p. 81 |

Single Model Reference | p. 82 |

Peformance Analysis | p. 82 |

Summary and Conclusion | p. 91 |

References | p. 91 |

Tracking Splitting Targets in Clutter by Using an Interacting Multiple Model Joint Probabilistic Data Association Filter | p. 93 |

Introduction | p. 93 |

The Approach | p. 94 |

The Models for the Splitting and Their State Estimation | p. 96 |

The Transitions Between the Models | p. 96 |

The "Just Split" Model | p. 97 |

The "Split" Model | p. 99 |

The Interaction Between the Models | p. 102 |

Simulation Results | p. 103 |

Conclusion | p. 110 |

References | p. 110 |

Precision Tracking of Small Extended Targets with Imaging Sensors | p. 111 |

Introduction | p. 111 |

Extraction of Measurements from an Imaging Sensor | p. 113 |

Modeling the Image | p. 114 |

Estimation of the Centroid | p. 115 |

The Offset Measurement from Image Correlation | p. 117 |

Application to a Gaussian Plume Target | p. 118 |

Precision Target Tracking of the Image Centroid | p. 120 |

Filter with White Measurement Noise Model | p. 121 |

Filter with Autocorrelated Noise Model | p. 122 |

Simulation Results | p. 124 |

Tracking Crossing Targets with FLIR Sensors | p. 125 |

Background | p. 125 |

Problem Formulation | p. 126 |

The State Estimation | p. 130 |

Simulation Results for Crossing Targets | p. 137 |

Derivations for the Centroid Estimate | p. 140 |

The Offset Measurement from Image Correlation | p. 143 |

Evaluation of the "Image-Mixing" Parameter | p. 146 |

References | p. 147 |

A System Approach to Multiple Target Tracking | p. 149 |

Introduction | p. 149 |

Measurement Pattern Optimization | p. 152 |

Waveform Optimization | p. 160 |

Resolution | p. 165 |

Fundamental Limits in Multiple Target Tracking | p. 173 |

Probabilities of Resolution and Data Association | p. 179 |

References | p. 180 |

Performance Analysis of Optimal Data Association with Applications to Multiple Target Tracking | p. 183 |

Introduction | p. 183 |

Problem Statement | p. 187 |

Probability of Correct Association | p. 190 |

Effects of Misassociation | p. 194 |

Effects of Extraneous Objects | p. 213 |

Application to Multitarget Tracking | p. 218 |

Conclusions | p. 227 |

Some Spherical Integrals | p. 228 |

Conditional Gaussian Distributions | p. 230 |

References | p. 233 |

Mutitarget Tracking with an Agile Beam Radar | p. 237 |

Introduction | p. 237 |

Performance Prediction | p. 238 |

Analytic Methods for Predicting Track Accuracy | p. 239 |

Analytic Methods for Predicting Correlation (Association) Performance | p. 240 |

Monte Carlo Simulation | p. 241 |

Detection: Observation Generation and Processing | p. 242 |

Enhancing Detection and Measurement Peformance | p. 242 |

Reducing the Effects of Jet Engine Modulation | p. 243 |

Radar Resource Allocation | p. 244 |

Choice of Optimal TOT | p. 245 |

Global Allocation Strategy | p. 249 |

Determining Task Figures of Merit | p. 250 |

Utility Theory Allocation | p. 251 |

Expert System Allocation | p. 254 |

Other Allocation Issues | p. 257 |

Typical Allocation Example | p. 258 |

Filtering and Prediction | p. 260 |

Choice of Tracking Coordinates and States | p. 260 |

Target Maneuver Modeling and Detection | p. 261 |

Modified Spherical Coordinates | p. 261 |

Data Association | p. 262 |

Conventional Data Association | p. 262 |

Multiple Hypothesis Tracking | p. 263 |

Joint Probabilistic Data Association | p. 264 |

Group Tracking | p. 264 |

Other Implementation Issues | p. 265 |

Other Future System Issues | p. 265 |

Track Confirmation for Low-Observable Targets | p. 265 |

Radar as Part of Multiple Sensor System | p. 266 |

Conclusion | p. 267 |

References | p. 267 |

Autonomous Navigation with Uncertain Reference Points Using the PDAF | p. 271 |

Introduction | p. 271 |

Autonomous Navigation Without Landmark Recognition | p. 272 |

Discrete-Time State and Observation Models | p. 272 |

Notation | p. 276 |

Measurement Validation Test | p. 277 |

Formulation of the Autonomous Navigation Filter | p. 278 |

Autonomous Navigation with landmark Recognition | p. 282 |

Inclusion of Bayesian Recognition Information | p. 282 |

Use of Uncertain Recognition Information | p. 286 |

Inclusion of a Detected Landmark Identity Classification | p. 302 |

Simulation Results | p. 314 |

Summary and Conclusions | p. 317 |

Calculation of the Association Probabilities for a Filter Using a Classifier | p. 319 |

References | p. 323 |

The Sensor Management Imperative | p. 325 |

Introduction | p. 325 |

Establishing the Sensor Management Imperative | p. 327 |

General Discussion | p. 328 |

Effective Use of Limited System Resources | p. 330 |

Track Maintenance | p. 332 |

Sensor Fusion and Synergism | p. 333 |

Situation Assessment | p. 334 |

Support of Specific Goals | p. 335 |

Adaptive Behavior in Varying Sensing Environments | p. 336 |

Summary | p. 336 |

Sensor Management Approaches | p. 336 |

Architectures for Sensor Management | p. 337 |

The Macro-Micro Architecture | p. 337 |

Scheduling Techniques | p. 343 |

Decision-Making Techniques | p. 347 |

Demonstrations of Sensor Management | p. 363 |

Demonstration 1 | p. 365 |

Demonstration 2 | p. 372 |

Demonstration 3 | p. 376 |

Demonstration 4 | p. 378 |

Demonstration 5 | p. 385 |

Conclusion | p. 389 |

References | p. 391 |

Attribute Fusion and Situation Assessment with a Many-Valued Logic Approach | p. 393 |

Introduction | p. 393 |

Aggregation Operators | p. 395 |

Conjunction and Propagation Using Triangular Norms | p. 395 |

Disjunction Using Triangular Conorms | p. 396 |

Relationships Between T-Norms and T-Conorms | p. 397 |

Negation Operators and Calculi of Uncertainty | p. 398 |

Families of T-Norms and T-Conorms | p. 400 |

Linguistic Variables Defined on the Interval [0, 1] | p. 402 |

Example of a Term Set of Linguistic Probabilities | p. 403 |

Description of the Experiments and Required Techniques | p. 404 |

The First Experiment | p. 404 |

The Second Experiment | p. 407 |

Computational Techniques | p. 408 |

Conclusions on the Theory Section | p. 411 |

Summary of the Results | p. 411 |

Impact of the Results on Expert System Technology | p. 412 |

Reasoning with Uncertainty--RUM and RUMrunner | p. 413 |

Introduction | p. 413 |

Applications of the RUM Technology | p. 415 |

Tactical and Surveillance Platform Applications | p. 416 |

The Airborne Fighter Problem | p. 416 |

The Surveillance Mission Problem | p. 419 |

Summary and Conclusions | p. 419 |

Properties of T-Norm Operators | p. 429 |

References | p. 432 |

Index | p. 435 |

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