知识辅助的空中目标综合识别

With the increasing complexity of modern battlefield environment and the upgrading of aviation equipment technology, massive multi-source heterogeneous sensor data inevitably appear inconsistent and incomplete problems. Traditional multi-sensor fusion method ignores sensor features correlation, and forms a closed data-driven recognition system of sensors. Whereas expert cognition, domain experience, attribute rules and other knowledge can instruct model construction and inference recognition of comprehensive target recognition in the form of expert experience, rule constraints and so on, this paper presents a method of knowledge assisted integrated identification of aerial targets. First of all, a military combat knowledge map of typical aerial target features is constructed, and key feature parameters are extracted to establish a target identification framework model. Then data basic trust assignment and evidence conflict credibility are constructed at recognition and decision recognition level respectively. Besides, time-domain fusion rules for high-conflict evidence is formulated to adjust timing fusion weights by using historical data. Finally, type recognition of multi-sensor is hierarchically realized through static reasoning and dynamic fusion. This study recognition accuracy is better than the existing algorithms in typical aerial target recognition tasks, demonstrating the effectiveness of the proposed algorithm.