Two-phase identification algorithm based on fuzzy set and voting for intelligent multi-sensor data fusion

• Authors: Sukhoon Kang
• Venue: KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
• Year: 2006

Multi-sensor data fusion techniques combine data from multiple sensors in order to get more accurate and efficient meaningful information through several intelligent process levels that may not be possible from a single sensor alone. One of the most important parts in the intelligent data fusion system is the identification fusion, and it can be categorized into physical models, parametric classification and cognitive-based models. In this paper, we present a novel identification fusion method by integrating two fusion approaches such as the parametric classification techniques and the cognitive-based models for achieving high intelligent decision support. We also have confirmed that the reliability and performance of two-phase identification algorithm never fall behind other fusion methods. We thus argue that our heuristics are required for effective decision making in real time for intelligent military situation assessment.