基于改进D-S理论在无人化武器系统信息融合中的应用

Application of Information Fusion in Unmanned Weapon System Based on Improved D-S Theory

针对无人化武器系统多传感器数据之间存在的信息融合问题,提出一种将AHP法和D-S证据理论相结合的无人化武器系统信息融合算法。根据多个传感器信息融合时所在环境条件对各传感器精确程度的影响,通过AHP法确定在多个传感器基本概率赋值中各传感器的权值,并利用D-S证据理论进行改进,实现了目标识别。实例分析结果证明:改进后的识别结果明显优于传统D-S证据理论的识别结果,在一定程度上改善了目标识别系统的性能。

Aiming at the information fusion problem among multi-sensor data of unmanned weapon system, put forward the unmanned weapon system information fusion algorithm based on combination of AHP method and D-S evidence theory. According to the influence of environment on sensor accuracy when multi-sensor information fusion, confirm every sensor value of multi-sensor basic probability evaluating by analytic hierarchy process （AHP） method, use D-S evidence theory for improvement and realize object recognizing. The experiment analysis result shows that improve identification results is well than tradition D-S evidence theory, and improve the performance of target identification system to a certain extent.