<title>Fuzzy evidential-reasoning-based decision fusion</title>

Abstract

The paper presents a new decision fusion methodology for identifying and tracking of multiple targets in a multisensor environment. The methodology combines concepts from the fuzzy logic and evidential reasoning domains to develop an integrated approach. The core methodology assumes that the sensors provide non-crisp or fuzzy labels, i.e., provide fuzzy class membership estimates corresponding to the different decision choices. However, the methodology can be adapted to environments wherein the sensors do not offer such information but only provide a single label deemed as the most likely. This is accomplished by having a learning phase wherein the performance of the sensors as compared to the ground truth is observed and utilized to derive fuzzy membership estimates corresponding to every individual sensor-decision scenario. The tracking mechanism is designed to also maintain fuzzy membership in different classes until the membership in any one class approaches unity. The fuzzy membership vectors, corresponding to the input sensor data as well as the tracks, include a measure of ignorance. This ignorance continuously decreases for the tracks as more and more track reports are integrated into the tracks. The paper presents details of the algorithmic process along with results as applied to some real-world data that demonstrates the effectiveness of the synergistic exploitation of the fuzzy logic and evidential reasoning concepts.