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In this report, we consider some design and analysis aspects of distributed detection networks. The main focus is on the Bayesian approach to the design of these systems. They present a computationally efficient approach to the design of decentralized Bayesian detection systems. This procedure is based upon an alternate representation of the minimum average cost in terms of a modified form of the Kolmogorov variational distance. They demonstrate the utility of our approach by applying it to the design and performance evaluation of four decentralized detection structures.

This article is from Sensors (Basel, Switzerland) , volume 11 . Abstract The lack of knowledge models to represent sensor systems, algorithms, and missions makes opportunistically discovering a synthesis of systems and algorithms that can satisfy high-level mission specifications impractical. A novel ontological problem-solving framework has been designed that leverages knowledge models describing sensors, algorithms, and high-level missions to facilitate automated inference of assigning systems to subtasks that may satisfy a given mission specification. To demonstrate the efficacy of the ontological problem-solving architecture, a family of persistence surveillance sensor systems and algorithms has been instantiated in a prototype environment to demonstrate the assignment of systems to subtasks of high-level missions.

A computational model of the deterministic and stochastic processes involved in multispectral remote sensing was designed to evaluate the performance of sensor systems and data processing algorithms for spectral feature classification. Accuracy in distinguishing between categories of surfaces or between specific types is developed as a means to compare sensor systems and data processing algorithms. The model allows studies to be made of the effects of variability of the atmosphere and of surface reflectance, as well as the effects of channel selection and sensor noise. Examples of these effects are shown.