10 Perspectives on artificial intelligence in sensor resources management
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Abstract
Sensor resource management methods rely on artificial intelligence techniques. This chapter presents a series of examples on how modern artificial intelligence techniques can lead to improvements in performance for smart sensors. The examples comprise the use of stochastic optimisation as a decision process, convex optimisation, and supervised learning for rapid quality of service (QoS) management, and a variational autoencoder as a generative model for radar waveform generation.
Abstract
Sensor resource management methods rely on artificial intelligence techniques. This chapter presents a series of examples on how modern artificial intelligence techniques can lead to improvements in performance for smart sensors. The examples comprise the use of stochastic optimisation as a decision process, convex optimisation, and supervised learning for rapid quality of service (QoS) management, and a variational autoencoder as a generative model for radar waveform generation.
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Foreword VII
- Preface IX
- Contents XXIII
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Part I Artificially intelligent sensing
- 1 Sound source classification using deep learning image classification networks 3
- 2 Advances in array calibration 19
- 3 Optimal sensor placement using genetic algorithms 37
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Part II Data-driven learning algorithms
- 4 Machine learning for electronic intelligence 59
- 5 Trajectory optimization with reinforcement learning 75
- 6 Data-driven state prediction and target tracking 93
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Part III Discussion of advanced applications
- 7 Track-before-detect for passive radar 109
- 8 Data fusion for reconnaissance of radio nuclides 125
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Part IV Managing multifunctional sensors
- 9 Multifunction RF sensor management 147
- 10 Perspectives on artificial intelligence in sensor resources management 161
- 11 Intelligent sensor network management 175
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Part V Quantum algorithms for data fusion
- 12 Quantum algorithms for data fusion 193
- 13 Indistinguishability and anti-symmetry in multiple target tracking 205
- 14 Quantum computing for solving data association problems 225
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Part VI Issues of certification and ethical alignment
- 15 Sensor data integrity in maritime multi-sensor networks 247
- 16 Explainable and certifiable AI 265
- 17 Ethical issues of AI-based sensing 275
- List of abbreviations
- Index 293
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Foreword VII
- Preface IX
- Contents XXIII
-
Part I Artificially intelligent sensing
- 1 Sound source classification using deep learning image classification networks 3
- 2 Advances in array calibration 19
- 3 Optimal sensor placement using genetic algorithms 37
-
Part II Data-driven learning algorithms
- 4 Machine learning for electronic intelligence 59
- 5 Trajectory optimization with reinforcement learning 75
- 6 Data-driven state prediction and target tracking 93
-
Part III Discussion of advanced applications
- 7 Track-before-detect for passive radar 109
- 8 Data fusion for reconnaissance of radio nuclides 125
-
Part IV Managing multifunctional sensors
- 9 Multifunction RF sensor management 147
- 10 Perspectives on artificial intelligence in sensor resources management 161
- 11 Intelligent sensor network management 175
-
Part V Quantum algorithms for data fusion
- 12 Quantum algorithms for data fusion 193
- 13 Indistinguishability and anti-symmetry in multiple target tracking 205
- 14 Quantum computing for solving data association problems 225
-
Part VI Issues of certification and ethical alignment
- 15 Sensor data integrity in maritime multi-sensor networks 247
- 16 Explainable and certifiable AI 265
- 17 Ethical issues of AI-based sensing 275
- List of abbreviations
- Index 293
