Overview of Book.- Stochastic Control Theory for Sensor Management.- Information Theoretic Approaches to Sensor Management.- Joint Multi-Target Particle Filtering.- POMDP Approximation Using Simulation and Heuristics.- Multi-Armed Bandit Problems.- Application of Multi-Armed Bandits to Sensor Management.- Active Learning and Sampling.- Plan-in-Advance Learning.- Sensor Scheduling in Radar.- Defense Applications.
This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field. The editors and contributors to this book are pioneers in the area of active sensing and sensor management, and represent the diverse communities that are targeted.
Active sensing is recognized as an enabling technology for the next generation of agile, multi-modal, and multi-waveform sensor platforms to efficiently perform tasks such as target detection, tracking, and identification. Recently, several research programs at DARPA (SWARMS, ISP), ARO (MURI), and AFOSR (ATR MURI) have funded efforts in areas related to active sensing. These resulted in focused efforts by several research groups in academia, government laboratories, and industry. These efforts have led to advances in theory and implementation that have borne some fruit in specific technology areas. For example, several promising new methods to approximate optimal multistage sensor management strategies for target tracking have been developed and an understanding of design challenges and performance tradeoffs is beginning to emerge. This book introduces the area, takes stock of these advances, and describes open problems and challenges in order to advance the field.