Task-directed multisensor fusion

The authors consider the problem of task-directed information gathering. They first develop a decision-theoretic model of task-directed sensing. In this framework, sensors are modeled as noise-contaminated, uncertain measurement systems. A sensor task is modelled as consisting of a function describing the type of information required by the task, a utility function describing sensitivity to error, and a cost function describing time or resource constraints on the system. From this description, the authors develop a computational method approximating a standard Bayesian decision-making model. This algorithm, which relies on a finite-element computation, is applicable to a wide variety of sensor fusion problems. The authors describe its derivation, analyze its error properties, and indicate how it can be made robust to errors in the description of sensors and discrepancies between geometric models and sensed objects. They also present the result of applying this fusion technique to several different information gathering tasks in simulated situations and in a distributed sensing system. >

Authors

• Gregory D. Hager
• Max Mintz

Keywords

• Sensor systems
• Sensor phenomena and characterization
• Measurement uncertainty
• Noise measurement
• Cost function
• Time factors
• Standards development
• Bayesian methods
• Decision making
• Finite element methods
• Estimation Error
• Distribution Of Variables
• Development Of Methods
• Finite Element
• Sample Distribution
• Parameter Space
• Prior Information
• Finite Element Method
• Unknown Parameters
• Mean Of Distribution
• Sampling Density
• Parametrized
• Model Uncertainty
• Types Of Errors
• Class Distribution
• Elements
• Information Gathering
• Tolerance Factor
• Fusion Method