Automatic target recognition:
"From an engineer designing Automatic Target Recognition (ATR) systems for 40 years, comes this step-by-step guide to producing state-of-the-art ATR systems. The full spectrum of ATR designs are covered, from systems that just suggest targets to the warfighter to ATRs that could serve as the &q...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bellingham, Washington, USA
SPIE Press
[2020]
|
| Ausgabe: | Fourth edition |
| Schriftenreihe: | Tutorial texts in optical engineering
volume TT120 |
| Schlagworte: | |
| Online-Zugang: | FHD01 URL des Erstveröffentlichers |
| Zusammenfassung: | "From an engineer designing Automatic Target Recognition (ATR) systems for 40 years, comes this step-by-step guide to producing state-of-the-art ATR systems. The full spectrum of ATR designs are covered, from systems that just suggest targets to the warfighter to ATRs that could serve as the "brains" of lethal autonomous robots. Unfortunately, when it comes to ATR, some practitioners claim that their off-the-shelf canned algorithms magically leap from academic research to deployment with scant domain knowledge or system engineering. Deep learning is marketed more than deep understanding, deep explainability or deep fusion of on-platform resources. Naïve practitioners twist a few algorithmic knobs, and test on data of uncertain virtue, until performance seems superb. Unfortunately, with the enemy and ever changing environment conspiring to defeat detection and recognition, naively designed ATRs can fail in unexpected and spectacular ways. Trustworthy ATRs need to fuse multiple data and metadata sources, continuously learn from and adapt to their environment, interact with humans in natural language, and deal with in-library and out-of-library targets and confusor objects. This book provides a blueprint for smarter, more autonomous, more sophisticated ATR designs"-- |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781510631205 |
| DOI: | 10.1117/3.2542436 |
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| 505 | 8 | |a Preface -- 1. Definitions and performance measures: 1.1. What is automatic target recognition (ATR)? 1.2. Basic definitions; 1.3. Detection criteria; 1.4. Performance measures for target detection; 1.5. Classification criteria; 1.6. Experimental design; 1.7. Characterizations of ATR hardware/software; References -- 2. Target detection strategies: 2.1. Introduction; 2.2. Simple detection algorithms; 2.3. More-complex detectors; 2.4. Grand paradigms; 2.5. Traditional SAR and hyperspectral target detectors; 2.6. Conclusions and future direction; References; Appendices -- 3. Target classifier strategies: 3.1. Introduction; 3.2. Main issues to consider in target classification; 3.3. Feature extraction; 3.4. Feature selection; 3.5. Examples of feature types; 3.6. Examples of classifiers; 3.7. Discussion; References -- 4. Unification of automatic target tracking and automatic target recognition: 4.1. Introduction; 4.2. Categories of tracking problems; 4.3. Tracking problems; 4.4. Extensions of target tracking; 4.5. Collaborative ATT and ATR (ATT<->ATR); 4.6. Unification of ATT and ATR (ATT<->ATR); 4.7. Discussion; References -- 5. Multisensor fusion: 5.1. Introduction; 5.2. Critical fusion issues related to ATR; 5.3. Levels of fusion; 5.4. Multiclassifier fusion; 5.5. Multisensor fusion based on multiclassifier fusion; 5.6. Test and evaluation; 5.7. Beyond basic ATR fusion; 5.8. Discussion; References | |
| 505 | 8 | |a 6. Next-generation ATR: 6.1. Introduction; 6.2. Hardware design; 6.3. Algorithm/software design; 6.4. Potential impact; References -- 7. How smart is your automatic target recognizer? 7.1. Introduction; 7.2. Test for determining the intelligence of an ATR; 7.3. Sentient versus sapient ATR; 7.4. Discussion: where is ATR headed? References -- 8. ATR and lethal autonomous robots: 8.1. Introduction; 8.2. Lethal autonomous robots; 8.3. ATR and LARs: moral, legal, and ethical perspectives; 8.4. LARs and the OODA loop; 8.5. Should LARs be characterized as AI, ATR, machine learning, neural networks, deep learning, or what? 8.6. LARs: evolutionary or revolutionary? 8.7. Can the LAR's ATR achieve human level performance? 8.8. LARs: what can go wrong? 8.9. Discussion; References -- Appendix 1: Resources -- Appendix 2: Questions to pose to the ATR customer -- Appendix 3: Acronyms and abbreviations -- Index | |
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| bvnumber | BV046693676 |
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| contents | Preface -- 1. Definitions and performance measures: 1.1. What is automatic target recognition (ATR)? 1.2. Basic definitions; 1.3. Detection criteria; 1.4. Performance measures for target detection; 1.5. Classification criteria; 1.6. Experimental design; 1.7. Characterizations of ATR hardware/software; References -- 2. Target detection strategies: 2.1. Introduction; 2.2. Simple detection algorithms; 2.3. More-complex detectors; 2.4. Grand paradigms; 2.5. Traditional SAR and hyperspectral target detectors; 2.6. Conclusions and future direction; References; Appendices -- 3. Target classifier strategies: 3.1. Introduction; 3.2. Main issues to consider in target classification; 3.3. Feature extraction; 3.4. Feature selection; 3.5. Examples of feature types; 3.6. Examples of classifiers; 3.7. Discussion; References -- 4. Unification of automatic target tracking and automatic target recognition: 4.1. Introduction; 4.2. Categories of tracking problems; 4.3. Tracking problems; 4.4. Extensions of target tracking; 4.5. Collaborative ATT and ATR (ATT<->ATR); 4.6. Unification of ATT and ATR (ATT<->ATR); 4.7. Discussion; References -- 5. Multisensor fusion: 5.1. Introduction; 5.2. Critical fusion issues related to ATR; 5.3. Levels of fusion; 5.4. Multiclassifier fusion; 5.5. Multisensor fusion based on multiclassifier fusion; 5.6. Test and evaluation; 5.7. Beyond basic ATR fusion; 5.8. Discussion; References 6. Next-generation ATR: 6.1. Introduction; 6.2. Hardware design; 6.3. Algorithm/software design; 6.4. Potential impact; References -- 7. How smart is your automatic target recognizer? 7.1. Introduction; 7.2. Test for determining the intelligence of an ATR; 7.3. Sentient versus sapient ATR; 7.4. Discussion: where is ATR headed? References -- 8. ATR and lethal autonomous robots: 8.1. Introduction; 8.2. Lethal autonomous robots; 8.3. ATR and LARs: moral, legal, and ethical perspectives; 8.4. LARs and the OODA loop; 8.5. Should LARs be characterized as AI, ATR, machine learning, neural networks, deep learning, or what? 8.6. LARs: evolutionary or revolutionary? 8.7. Can the LAR's ATR achieve human level performance? 8.8. LARs: what can go wrong? 8.9. Discussion; References -- Appendix 1: Resources -- Appendix 2: Questions to pose to the ATR customer -- Appendix 3: Acronyms and abbreviations -- Index |
| ctrlnum | (OCoLC)1152224224 (DE-599)BVBBV046693676 |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1117/3.2542436 |
| edition | Fourth edition |
| format | Electronic eBook |
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| id | DE-604.BV046693676 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:25:57Z |
| indexdate | 2024-07-10T08:51:18Z |
| institution | BVB |
| isbn | 9781510631205 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032104384 |
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| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | SPIE Press |
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| series | Tutorial texts in optical engineering |
| series2 | Tutorial texts in optical engineering |
| spelling | Schachter, Bruce J. 1946- Verfasser (DE-588)172394708 aut Automatic target recognition Bruce J. Schachter Fourth edition Bellingham, Washington, USA SPIE Press [2020] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Tutorial texts in optical engineering volume TT120 Preface -- 1. Definitions and performance measures: 1.1. What is automatic target recognition (ATR)? 1.2. Basic definitions; 1.3. Detection criteria; 1.4. Performance measures for target detection; 1.5. Classification criteria; 1.6. Experimental design; 1.7. Characterizations of ATR hardware/software; References -- 2. Target detection strategies: 2.1. Introduction; 2.2. Simple detection algorithms; 2.3. More-complex detectors; 2.4. Grand paradigms; 2.5. Traditional SAR and hyperspectral target detectors; 2.6. Conclusions and future direction; References; Appendices -- 3. Target classifier strategies: 3.1. Introduction; 3.2. Main issues to consider in target classification; 3.3. Feature extraction; 3.4. Feature selection; 3.5. Examples of feature types; 3.6. Examples of classifiers; 3.7. Discussion; References -- 4. Unification of automatic target tracking and automatic target recognition: 4.1. Introduction; 4.2. Categories of tracking problems; 4.3. Tracking problems; 4.4. Extensions of target tracking; 4.5. Collaborative ATT and ATR (ATT<->ATR); 4.6. Unification of ATT and ATR (ATT<->ATR); 4.7. Discussion; References -- 5. Multisensor fusion: 5.1. Introduction; 5.2. Critical fusion issues related to ATR; 5.3. Levels of fusion; 5.4. Multiclassifier fusion; 5.5. Multisensor fusion based on multiclassifier fusion; 5.6. Test and evaluation; 5.7. Beyond basic ATR fusion; 5.8. Discussion; References 6. Next-generation ATR: 6.1. Introduction; 6.2. Hardware design; 6.3. Algorithm/software design; 6.4. Potential impact; References -- 7. How smart is your automatic target recognizer? 7.1. Introduction; 7.2. Test for determining the intelligence of an ATR; 7.3. Sentient versus sapient ATR; 7.4. Discussion: where is ATR headed? References -- 8. ATR and lethal autonomous robots: 8.1. Introduction; 8.2. Lethal autonomous robots; 8.3. ATR and LARs: moral, legal, and ethical perspectives; 8.4. LARs and the OODA loop; 8.5. Should LARs be characterized as AI, ATR, machine learning, neural networks, deep learning, or what? 8.6. LARs: evolutionary or revolutionary? 8.7. Can the LAR's ATR achieve human level performance? 8.8. LARs: what can go wrong? 8.9. Discussion; References -- Appendix 1: Resources -- Appendix 2: Questions to pose to the ATR customer -- Appendix 3: Acronyms and abbreviations -- Index "From an engineer designing Automatic Target Recognition (ATR) systems for 40 years, comes this step-by-step guide to producing state-of-the-art ATR systems. The full spectrum of ATR designs are covered, from systems that just suggest targets to the warfighter to ATRs that could serve as the "brains" of lethal autonomous robots. Unfortunately, when it comes to ATR, some practitioners claim that their off-the-shelf canned algorithms magically leap from academic research to deployment with scant domain knowledge or system engineering. Deep learning is marketed more than deep understanding, deep explainability or deep fusion of on-platform resources. Naïve practitioners twist a few algorithmic knobs, and test on data of uncertain virtue, until performance seems superb. Unfortunately, with the enemy and ever changing environment conspiring to defeat detection and recognition, naively designed ATRs can fail in unexpected and spectacular ways. Trustworthy ATRs need to fuse multiple data and metadata sources, continuously learn from and adapt to their environment, interact with humans in natural language, and deal with in-library and out-of-library targets and confusor objects. This book provides a blueprint for smarter, more autonomous, more sophisticated ATR designs"-- Radar targets Optical pattern recognition Algorithms Image processing Radar (DE-588)4176765-2 gnd rswk-swf Zielerkennung (DE-588)4190792-9 gnd rswk-swf Objektverfolgung (DE-588)4311226-2 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Objektverfolgung (DE-588)4311226-2 s Bildverarbeitung (DE-588)4006684-8 s Mustererkennung (DE-588)4040936-3 s 1\p DE-604 Radar (DE-588)4176765-2 s Zielerkennung (DE-588)4190792-9 s 2\p DE-604 Erscheint auch als Online-Ausgabe, kindle edition 978-1-5106-3122-9 Erscheint auch als Online-Ausgabe, epub 978-1-5106-3121-2 Erscheint auch als Druck-Ausgabe, paperback 978-1-5106-3119-9 Tutorial texts in optical engineering volume TT120 (DE-604)BV044913200 120 https://doi.org/10.1117/3.2542436 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Schachter, Bruce J. 1946- Automatic target recognition Tutorial texts in optical engineering Preface -- 1. Definitions and performance measures: 1.1. What is automatic target recognition (ATR)? 1.2. Basic definitions; 1.3. Detection criteria; 1.4. Performance measures for target detection; 1.5. Classification criteria; 1.6. Experimental design; 1.7. Characterizations of ATR hardware/software; References -- 2. Target detection strategies: 2.1. Introduction; 2.2. Simple detection algorithms; 2.3. More-complex detectors; 2.4. Grand paradigms; 2.5. Traditional SAR and hyperspectral target detectors; 2.6. Conclusions and future direction; References; Appendices -- 3. Target classifier strategies: 3.1. Introduction; 3.2. Main issues to consider in target classification; 3.3. Feature extraction; 3.4. Feature selection; 3.5. Examples of feature types; 3.6. Examples of classifiers; 3.7. Discussion; References -- 4. Unification of automatic target tracking and automatic target recognition: 4.1. Introduction; 4.2. Categories of tracking problems; 4.3. Tracking problems; 4.4. Extensions of target tracking; 4.5. Collaborative ATT and ATR (ATT<->ATR); 4.6. Unification of ATT and ATR (ATT<->ATR); 4.7. Discussion; References -- 5. Multisensor fusion: 5.1. Introduction; 5.2. Critical fusion issues related to ATR; 5.3. Levels of fusion; 5.4. Multiclassifier fusion; 5.5. Multisensor fusion based on multiclassifier fusion; 5.6. Test and evaluation; 5.7. Beyond basic ATR fusion; 5.8. Discussion; References 6. Next-generation ATR: 6.1. Introduction; 6.2. Hardware design; 6.3. Algorithm/software design; 6.4. Potential impact; References -- 7. How smart is your automatic target recognizer? 7.1. Introduction; 7.2. Test for determining the intelligence of an ATR; 7.3. Sentient versus sapient ATR; 7.4. Discussion: where is ATR headed? References -- 8. ATR and lethal autonomous robots: 8.1. Introduction; 8.2. Lethal autonomous robots; 8.3. ATR and LARs: moral, legal, and ethical perspectives; 8.4. LARs and the OODA loop; 8.5. Should LARs be characterized as AI, ATR, machine learning, neural networks, deep learning, or what? 8.6. LARs: evolutionary or revolutionary? 8.7. Can the LAR's ATR achieve human level performance? 8.8. LARs: what can go wrong? 8.9. Discussion; References -- Appendix 1: Resources -- Appendix 2: Questions to pose to the ATR customer -- Appendix 3: Acronyms and abbreviations -- Index Radar targets Optical pattern recognition Algorithms Image processing Radar (DE-588)4176765-2 gnd Zielerkennung (DE-588)4190792-9 gnd Objektverfolgung (DE-588)4311226-2 gnd Bildverarbeitung (DE-588)4006684-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4176765-2 (DE-588)4190792-9 (DE-588)4311226-2 (DE-588)4006684-8 (DE-588)4040936-3 |
| title | Automatic target recognition |
| title_auth | Automatic target recognition |
| title_exact_search | Automatic target recognition |
| title_exact_search_txtP | Automatic target recognition |
| title_full | Automatic target recognition Bruce J. Schachter |
| title_fullStr | Automatic target recognition Bruce J. Schachter |
| title_full_unstemmed | Automatic target recognition Bruce J. Schachter |
| title_short | Automatic target recognition |
| title_sort | automatic target recognition |
| topic | Radar targets Optical pattern recognition Algorithms Image processing Radar (DE-588)4176765-2 gnd Zielerkennung (DE-588)4190792-9 gnd Objektverfolgung (DE-588)4311226-2 gnd Bildverarbeitung (DE-588)4006684-8 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Radar targets Optical pattern recognition Algorithms Image processing Radar Zielerkennung Objektverfolgung Bildverarbeitung Mustererkennung |
| url | https://doi.org/10.1117/3.2542436 |
| volume_link | (DE-604)BV044913200 |
| work_keys_str_mv | AT schachterbrucej automatictargetrecognition |