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Advances in Angle-Only Filtering and Tracking in Two and Three Dimensions / / Mahendra Mallick, Ratnasingham Tharmarasa, editor
| Advances in Angle-Only Filtering and Tracking in Two and Three Dimensions / / Mahendra Mallick, Ratnasingham Tharmarasa, editor |
| Pubbl/distr/stampa | [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 |
| Descrizione fisica | 1 online resource |
| Disciplina | 660.284245 |
| Soggetto topico | Filters and filtration |
| ISBN | 3-0365-6855-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910719774403321 |
| [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integrated tracking, classification, and sensor management : theory and applications / / edited by Mahendra Mallick, Vikram Krishnamurthy, Ba-Ngu Vo
| Integrated tracking, classification, and sensor management : theory and applications / / edited by Mahendra Mallick, Vikram Krishnamurthy, Ba-Ngu Vo |
| Pubbl/distr/stampa | Hoboken, N.J., : IEEE Press, 2013 |
| Descrizione fisica | 1 online resource (738 p.) |
| Disciplina | 681.2 |
| Altri autori (Persone) |
MallickMahendra
KrishnamurthyV (Vikram) VoBa-Ngu |
| Soggetto topico |
Detectors
Signal processing - Digital techniques Bayesian statistical decision theory |
| ISBN |
1-118-45055-8
1-283-83504-5 1-118-45060-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | pt. I. Filtering -- pt. II. Multitarget multisensor tracking -- pt. III. Sensor management and control -- pt. IV. Estimation and classification -- pt. V. Decision fusion and decision support. |
| Record Nr. | UNINA-9910877469603321 |
| Hoboken, N.J., : IEEE Press, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integrated tracking, classification, and sensor management : theory and applications / / edited by Mahendra Mallick, Vikram Krishnamurthy, Ba-Ngu Vo
| Integrated tracking, classification, and sensor management : theory and applications / / edited by Mahendra Mallick, Vikram Krishnamurthy, Ba-Ngu Vo |
| Pubbl/distr/stampa | Oxford : , : Wiley-Blackwell, , 2012 |
| Descrizione fisica | 1 online resource (738 p.) |
| Disciplina | 681.2 |
| Altri autori (Persone) |
MallickMahendra
KrishnamurthyV (Vikram) VoBa-Ngu |
| Soggetto topico |
Detectors
Signal processing - Digital techniques Bayesian statistical decision theory |
| ISBN |
1-118-45055-8
1-283-83504-5 1-118-45060-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- PREFACE xvii -- CONTRIBUTORS xxiii -- PART I FILTERING -- 1. Angle-Only Filtering in Three Dimensions 3 / Mahendra Mallick, Mark Morelande, Lyudmila Mihaylova, Sanjeev Arulampalam, and Yanjun Yan -- 1.1 Introduction 3 -- 1.2 Statement of Problem 6 -- 1.3 Tracker and Sensor Coordinate Frames 6 -- 1.4 Coordinate Systems for Target and Ownship States 7 -- 1.5 Dynamic Models 9 -- 1.6 Measurement Models 14 -- 1.7 Filter Initialization 15 -- 1.8 Extended Kalman Filters 17 -- 1.9 Unscented Kalman Filters 19 -- 1.10 Particle Filters 23 -- 1.11 Numerical Simulations and Results 28 -- 1.12 Conclusions 31 -- 2. Particle Filtering Combined with Interval Methods for Tracking Applications 43 / Amadou Gning, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic / / 2.1 Introduction 43 -- 2.2 Related Works 44 -- 2.3 Interval Analysis 46 -- 2.4 Bayesian Filtering 51 -- 2.5 Box Particle Filtering 52 -- 2.6 Box Particle Filtering Derived from the Bayesian Inference Using a Mixture of Uniform Probability Density Functions 56 -- 2.7 Box-PF Illustration over a Target Tracking Example 65 -- 2.8 Application for a Vehicle Dynamic Localization Problem 67 -- 2.9 Conclusions 71 -- 3. Bayesian Multiple Target Filtering Using Random Finite Sets 75 / Ba-Ngu Vo, Ba-Tuong Vo, and Daniel Clark -- 3.1 Introduction 75 -- 3.2 Overview of the Random Finite Set Approach to Multitarget Filtering 76 -- 3.3 Random Finite Sets 81 -- 3.4 Multiple Target Filtering and Estimation 85 -- 3.5 Multitarget Miss Distances 91 -- 3.6 The Probability Hypothesis Density (PHD) Filter 95 -- 3.7 The Cardinalized PHD Filter 105 -- 3.8 Numerical Examples 111 -- 3.9 MeMBer Filter 117 -- 4. The Continuous Time Roots of the Interacting Multiple Model Filter 127 / Henk A.P. Blom -- 4.1 Introduction 127 -- 4.2 Hidden Markov Model Filter 129 -- 4.3 System with Markovian Coefficients 136 -- 4.4 Markov Jump Linear System 141 -- 4.5 Continuous-Discrete Filtering 149 -- 4.6 Concluding Remarks 154 -- PART II MULTITARGET MULTISENSOR TRACKING.
5. Multitarget Tracking Using Multiple Hypothesis Tracking 165 / Mahendra Mallick, Stefano Coraluppi, and Craig Carthel -- 5.1 Introduction 165 -- 5.2 Tracking Algorithms 166 -- 5.3 Track Filtering 170 -- 5.4 MHT Algorithms 179 -- 5.5 Hybrid-State Derivations of MHT Equations 180 -- 5.6 The Target-Death Problem 185 -- 5.7 Examples for MHT 186 -- 5.8 Summary 189 -- 6. Tracking and Data Fusion for Ground Surveillance 203 / Michael Mertens, Michael Feldmann, Martin Ulmke, and Wolfgang Koch -- 6.1 Introduction to Ground Surveillance 203 -- 6.2 GMTI Sensor Model 204 -- 6.3 Bayesian Approach to Ground Moving Target Tracking 209 -- 6.4 Exploitation of Road Network Data 222 -- 6.5 Convoy Track Maintenance Using Random Matrices 234 -- 6.6 Convoy Tracking with the Cardinalized Probability Hypothesis Density Filter 243 -- 7. Performance Bounds for Target Tracking: Computationally Efficient Formulations and Associated Applications 255 / Marcel Hernandez -- 7.1 Introduction 255 -- 7.2 Bayesian Performance Bounds 258 -- 7.3 PCRLB Formulations in Cluttered Environments 262 -- 7.4 An Approximate PCRLB for Maneuevring Target Tracking 269 -- 7.5 A General Framework for the Deployment of Stationary Sensors 271 -- 7.6 UAV Trajectory Planning 294 -- 7.7 Summary and Conclusions 305 -- 8. Track-Before-Detect Techniques 311 / Samuel J. Davey, Mark G. Rutten, and Neil J. Gordon -- 8.1 Introduction 311 -- 8.2 Models 318 -- 8.3 Baum Welch Algorithm 327 -- 8.4 Dynamic Programming: Viterbi Algorithm 331 -- 8.5 Particle Filter 334 -- 8.6 ML-PDA 337 -- 8.7 H-PMHT 341 -- 8.8 Performance Analysis 347 -- 8.9 Applications: Radar and IRST Fusion 354 -- 8.10 Future Directions 357 -- 9. Advances in Data Fusion Architectures 363 / Stefano Coraluppi and Craig Carthel -- 9.1 Introduction 363 -- 9.2 Dense-Target Scenarios 364 -- 9.3 Multiscale Sensor Scenarios 368 -- 9.4 Tracking in Large Sensor Networks 370 -- 9.5 Multiscale Objects 372 -- 9.6 Measurement Aggregation 378 -- 9.7 Conclusions 383 -- 10. Intent Inference and Detection of Anomalous Trajectories: A Metalevel Tracking Approach 387 / Vikram Krishnamurthy. 10.1 Introduction 387 -- 10.2 Anomalous Trajectory Classification Framework 393 -- 10.3 Trajectory Modeling and Inference Using Stochastic Context-Free Grammars 395 -- 10.4 Trajectory Modeling and Inference Using Reciprocal Processes (RP) 403 -- 10.5 Example 1: Metalevel Tracking for GMTI Radar 406 -- 10.6 Example 2: Data Fusion in a Multicamera Network 407 -- 10.7 Conclusion 413 -- PART III SENSOR MANAGEMENT AND CONTROL -- 11. Radar Resource Management for Target Tracking - A Stochastic Control Approach 417 / Vikram Krishnamurthy -- 11.1 Introduction 417 -- 11.2 Problem Formulation 422 -- 11.3 Structural Results and Lattice Programming for Micromanagement 431 -- 11.4 Radar Scheduling for Maneuvering Targets Modeled as Jump Markov Linear System 437 -- 11.5 Summary 444 -- 12. Sensor Management for Large-Scale Multisensor-Multitarget Tracking 447 / Ratnasingham Tharmarasa and Thia Kirubarajan -- 12.1 Introduction 447 -- 12.2 Target Tracking Architectures 451 -- 12.3 Posterior Cram'er / Rao Lower Bound 452 -- 12.4 Sensor Array Management for Centralized Tracking 458 -- 12.5 Sensor Array Management for Distributed Tracking 473 -- 12.6 Sensor Array Management for Decentralized Tracking 489 -- 12.7 Conclusions 507 -- PART IV ESTIMATION AND CLASSIFICATION -- 13. Efficient Inference in General Hybrid Bayesian Networks for Classification 523 / Wei Sun and Kuo-Chu Chang -- 13.1 Introduction 523 -- 13.2 Message Passing: Representation and Propagation 526 -- 13.3 Network Partition and Message Integration for Hybrid Model 532 -- 13.4 Hybrid Message Passing Algorithm for Classification 536 -- 13.5 Numerical Experiments 537 -- 13.6 Concluding Remarks 544 -- 14. Evaluating Multisensor Classification Performance with Bayesian Networks 547 / Eswar Sivaraman and Kuo-Chu Chang -- 14.1 Introduction 547 -- 14.2 Single-Sensor Model 548 -- 14.3 Multisensor Fusion Systems - Design and Performance Evaluation 560 -- 14.4 Summary and Continuing Questions 564 -- 15. Detection and Estimation of Radiological Sources 579 / Mark Morelande and Branko Ristic. 15.1 Introduction 579 -- 15.2 Estimation of Point Sources 580 -- 15.3 Estimation of Distributed Sources 590 -- 15.4 Searching for Point Sources 599 -- 15.5 Conclusions 612 -- PART V DECISION FUSION AND DECISION SUPPORT -- 16. Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks 619 / Qi Cheng, Ruixin Niu, Ashok Sundaresan, and Pramod K. Varshney -- 16.1 Introduction 619 -- 16.2 Elements of Detection Theory 620 -- 16.3 Distributed Detection with Multiple Sensors 624 -- 16.4 Distributed Detection in Wireless Sensor Networks 634 -- 16.5 Copula-Based Fusion of Correlated Decisions 645 -- 16.6 Conclusion 652 -- 17. Evidential Networks for Decision Support in Surveillance Systems 661 / Alessio Benavoli and Branko Ristic -- 17.1 Introduction 661 -- 17.2 Valuation Algebras 662 -- 17.3 Local Computation in a VA 668 -- 17.4 Theory of Evidence as a Valuation Algebra 672 -- 17.5 Examples of Decision Support Systems 685 -- References 702 -- Index 705. |
| Record Nr. | UNINA-9910141368703321 |
| Oxford : , : Wiley-Blackwell, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integrated tracking, classification, and sensor management : theory and applications / / edited by Mahendra Mallick, Vikram Krishnamurthy, Ba-Ngu Vo
| Integrated tracking, classification, and sensor management : theory and applications / / edited by Mahendra Mallick, Vikram Krishnamurthy, Ba-Ngu Vo |
| Pubbl/distr/stampa | Oxford : , : Wiley-Blackwell, , 2012 |
| Descrizione fisica | 1 online resource (738 p.) |
| Disciplina | 681.2 |
| Altri autori (Persone) |
MallickMahendra
KrishnamurthyV (Vikram) VoBa-Ngu |
| Soggetto topico |
Detectors
Signal processing - Digital techniques Bayesian statistical decision theory |
| ISBN |
1-118-45055-8
1-283-83504-5 1-118-45060-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
-- PREFACE xvii -- CONTRIBUTORS xxiii -- PART I FILTERING -- 1. Angle-Only Filtering in Three Dimensions 3 / Mahendra Mallick, Mark Morelande, Lyudmila Mihaylova, Sanjeev Arulampalam, and Yanjun Yan -- 1.1 Introduction 3 -- 1.2 Statement of Problem 6 -- 1.3 Tracker and Sensor Coordinate Frames 6 -- 1.4 Coordinate Systems for Target and Ownship States 7 -- 1.5 Dynamic Models 9 -- 1.6 Measurement Models 14 -- 1.7 Filter Initialization 15 -- 1.8 Extended Kalman Filters 17 -- 1.9 Unscented Kalman Filters 19 -- 1.10 Particle Filters 23 -- 1.11 Numerical Simulations and Results 28 -- 1.12 Conclusions 31 -- 2. Particle Filtering Combined with Interval Methods for Tracking Applications 43 / Amadou Gning, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic / / 2.1 Introduction 43 -- 2.2 Related Works 44 -- 2.3 Interval Analysis 46 -- 2.4 Bayesian Filtering 51 -- 2.5 Box Particle Filtering 52 -- 2.6 Box Particle Filtering Derived from the Bayesian Inference Using a Mixture of Uniform Probability Density Functions 56 -- 2.7 Box-PF Illustration over a Target Tracking Example 65 -- 2.8 Application for a Vehicle Dynamic Localization Problem 67 -- 2.9 Conclusions 71 -- 3. Bayesian Multiple Target Filtering Using Random Finite Sets 75 / Ba-Ngu Vo, Ba-Tuong Vo, and Daniel Clark -- 3.1 Introduction 75 -- 3.2 Overview of the Random Finite Set Approach to Multitarget Filtering 76 -- 3.3 Random Finite Sets 81 -- 3.4 Multiple Target Filtering and Estimation 85 -- 3.5 Multitarget Miss Distances 91 -- 3.6 The Probability Hypothesis Density (PHD) Filter 95 -- 3.7 The Cardinalized PHD Filter 105 -- 3.8 Numerical Examples 111 -- 3.9 MeMBer Filter 117 -- 4. The Continuous Time Roots of the Interacting Multiple Model Filter 127 / Henk A.P. Blom -- 4.1 Introduction 127 -- 4.2 Hidden Markov Model Filter 129 -- 4.3 System with Markovian Coefficients 136 -- 4.4 Markov Jump Linear System 141 -- 4.5 Continuous-Discrete Filtering 149 -- 4.6 Concluding Remarks 154 -- PART II MULTITARGET MULTISENSOR TRACKING.
5. Multitarget Tracking Using Multiple Hypothesis Tracking 165 / Mahendra Mallick, Stefano Coraluppi, and Craig Carthel -- 5.1 Introduction 165 -- 5.2 Tracking Algorithms 166 -- 5.3 Track Filtering 170 -- 5.4 MHT Algorithms 179 -- 5.5 Hybrid-State Derivations of MHT Equations 180 -- 5.6 The Target-Death Problem 185 -- 5.7 Examples for MHT 186 -- 5.8 Summary 189 -- 6. Tracking and Data Fusion for Ground Surveillance 203 / Michael Mertens, Michael Feldmann, Martin Ulmke, and Wolfgang Koch -- 6.1 Introduction to Ground Surveillance 203 -- 6.2 GMTI Sensor Model 204 -- 6.3 Bayesian Approach to Ground Moving Target Tracking 209 -- 6.4 Exploitation of Road Network Data 222 -- 6.5 Convoy Track Maintenance Using Random Matrices 234 -- 6.6 Convoy Tracking with the Cardinalized Probability Hypothesis Density Filter 243 -- 7. Performance Bounds for Target Tracking: Computationally Efficient Formulations and Associated Applications 255 / Marcel Hernandez -- 7.1 Introduction 255 -- 7.2 Bayesian Performance Bounds 258 -- 7.3 PCRLB Formulations in Cluttered Environments 262 -- 7.4 An Approximate PCRLB for Maneuevring Target Tracking 269 -- 7.5 A General Framework for the Deployment of Stationary Sensors 271 -- 7.6 UAV Trajectory Planning 294 -- 7.7 Summary and Conclusions 305 -- 8. Track-Before-Detect Techniques 311 / Samuel J. Davey, Mark G. Rutten, and Neil J. Gordon -- 8.1 Introduction 311 -- 8.2 Models 318 -- 8.3 Baum Welch Algorithm 327 -- 8.4 Dynamic Programming: Viterbi Algorithm 331 -- 8.5 Particle Filter 334 -- 8.6 ML-PDA 337 -- 8.7 H-PMHT 341 -- 8.8 Performance Analysis 347 -- 8.9 Applications: Radar and IRST Fusion 354 -- 8.10 Future Directions 357 -- 9. Advances in Data Fusion Architectures 363 / Stefano Coraluppi and Craig Carthel -- 9.1 Introduction 363 -- 9.2 Dense-Target Scenarios 364 -- 9.3 Multiscale Sensor Scenarios 368 -- 9.4 Tracking in Large Sensor Networks 370 -- 9.5 Multiscale Objects 372 -- 9.6 Measurement Aggregation 378 -- 9.7 Conclusions 383 -- 10. Intent Inference and Detection of Anomalous Trajectories: A Metalevel Tracking Approach 387 / Vikram Krishnamurthy. 10.1 Introduction 387 -- 10.2 Anomalous Trajectory Classification Framework 393 -- 10.3 Trajectory Modeling and Inference Using Stochastic Context-Free Grammars 395 -- 10.4 Trajectory Modeling and Inference Using Reciprocal Processes (RP) 403 -- 10.5 Example 1: Metalevel Tracking for GMTI Radar 406 -- 10.6 Example 2: Data Fusion in a Multicamera Network 407 -- 10.7 Conclusion 413 -- PART III SENSOR MANAGEMENT AND CONTROL -- 11. Radar Resource Management for Target Tracking - A Stochastic Control Approach 417 / Vikram Krishnamurthy -- 11.1 Introduction 417 -- 11.2 Problem Formulation 422 -- 11.3 Structural Results and Lattice Programming for Micromanagement 431 -- 11.4 Radar Scheduling for Maneuvering Targets Modeled as Jump Markov Linear System 437 -- 11.5 Summary 444 -- 12. Sensor Management for Large-Scale Multisensor-Multitarget Tracking 447 / Ratnasingham Tharmarasa and Thia Kirubarajan -- 12.1 Introduction 447 -- 12.2 Target Tracking Architectures 451 -- 12.3 Posterior Cram'er / Rao Lower Bound 452 -- 12.4 Sensor Array Management for Centralized Tracking 458 -- 12.5 Sensor Array Management for Distributed Tracking 473 -- 12.6 Sensor Array Management for Decentralized Tracking 489 -- 12.7 Conclusions 507 -- PART IV ESTIMATION AND CLASSIFICATION -- 13. Efficient Inference in General Hybrid Bayesian Networks for Classification 523 / Wei Sun and Kuo-Chu Chang -- 13.1 Introduction 523 -- 13.2 Message Passing: Representation and Propagation 526 -- 13.3 Network Partition and Message Integration for Hybrid Model 532 -- 13.4 Hybrid Message Passing Algorithm for Classification 536 -- 13.5 Numerical Experiments 537 -- 13.6 Concluding Remarks 544 -- 14. Evaluating Multisensor Classification Performance with Bayesian Networks 547 / Eswar Sivaraman and Kuo-Chu Chang -- 14.1 Introduction 547 -- 14.2 Single-Sensor Model 548 -- 14.3 Multisensor Fusion Systems - Design and Performance Evaluation 560 -- 14.4 Summary and Continuing Questions 564 -- 15. Detection and Estimation of Radiological Sources 579 / Mark Morelande and Branko Ristic. 15.1 Introduction 579 -- 15.2 Estimation of Point Sources 580 -- 15.3 Estimation of Distributed Sources 590 -- 15.4 Searching for Point Sources 599 -- 15.5 Conclusions 612 -- PART V DECISION FUSION AND DECISION SUPPORT -- 16. Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks 619 / Qi Cheng, Ruixin Niu, Ashok Sundaresan, and Pramod K. Varshney -- 16.1 Introduction 619 -- 16.2 Elements of Detection Theory 620 -- 16.3 Distributed Detection with Multiple Sensors 624 -- 16.4 Distributed Detection in Wireless Sensor Networks 634 -- 16.5 Copula-Based Fusion of Correlated Decisions 645 -- 16.6 Conclusion 652 -- 17. Evidential Networks for Decision Support in Surveillance Systems 661 / Alessio Benavoli and Branko Ristic -- 17.1 Introduction 661 -- 17.2 Valuation Algebras 662 -- 17.3 Local Computation in a VA 668 -- 17.4 Theory of Evidence as a Valuation Algebra 672 -- 17.5 Examples of Decision Support Systems 685 -- References 702 -- Index 705. |
| Record Nr. | UNINA-9910677370303321 |
| Oxford : , : Wiley-Blackwell, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||