Sensor and Data Fusion Algorithms and Architectures for Defense and Non-Defense Applications

What is it about?

High interest continues in defense usage of data fusion to assist in the identification of missile threats and other strategic and tactical targets, assessment of information, evaluation of potential responses to a threat, and allocation of resources. The signature-generation phenomena and fusion architectures and algorithms presented are applicable to these areas and the growing number of non-defense applications. The book chapters provide discussions of the benefits of infrared and millimeter-wave sensor operation including atmospheric effects; multiple-sensor system applications; and definitions and examples of sensor and data fusion architectures and algorithms. Data fusion algorithms discussed in detail include classical inference, which forms a foundation for the more general Bayesian inference and Dempster–Shafer evidential theory that follow; artificial neural networks; voting logic as derived from Boolean algebra expressions; fuzzy logic; and Kalman filtering. Descriptions are provided of multiple-radar tracking systems and architectures, and detection and tracking of objects using only passively acquired data. The book concludes with a summary of the information required to implement each of the data fusion methods discussed.

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Why is it important?

Multi-sensor systems are frequently deployed to assist with civilian and defense applications such as weather forecasting, Earth resource monitoring, traffic and transportation management, battlefield assessment, and target classification and tracking. They can be especially effective in defense applications where volume constraints associated with smart-weapons design are of concern and where combining and assessing information from noncollocated or dissimilar sensors and other data sources is critical.

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