Novel architecture for expert-assisted decision-level fusion

In this paper, we discuss a new fusion architecture, including some preliminary results on field data. The architecture consists of a new decision level fusion algorithm, the piecewise level fusion algorithm (PLFA), integrated with a new expert system based user assistant that adjusts PLFA parameters to optimize for a user desired classification performance. This architecture is applicable for both multisensor and multilook fusion. The user specifies classification performance by inputting entries for a desired confusion matrix at the fusion center. The intelligent assistant suggests input alternatives to reach the performance goal based on previously supplied user inputs and on performance specifications of the individual sensors. If deadlock results, i.e., the goal is not attainable because of conflicting user inputs, the assistant will inform the user. As the user and assistant interact, the assistant calculates the parameters necessary to automatically adjust the PLFA for the required performance. These parameters and calculations are hidden from the user. That is, the architecture is designed so that user inputs are intuitive for an unskilled operator. The implementation of this adaptable fusion architecture is due to the relatively simple structure of the PLFA and the expert system heuristic rules. We briefly describe the PLFA structure and operation, illustrate some expert system rules, and discuss preliminary performance of the entire architecture, including a sample dialogue between the user and the intelligent assistant. We conclude this paper with a discussion of future extensions to this architecture that include replacing human interactions with dynamic learning techniques.