Abstract: To the influence of distance between observation station and target on tracking performance, a measurement fusion algorithm based on fuzzy distance threshold for active and passive sensors is introduced to the target tracking system. The method choosing data fusion module based on distance parameter is discussed, and exponential functions and fuzzy processing are used to compute real-time weight of each sensor in measurement fusion process through priori knowledge. Simulation shows that the adaptive measurement fusion algorithm is more stable and can bring all complementary characteristic into full play of active and passive sensors compared to traditional invariable-weight method when random errors caused by target distance cannot be ignored.

Key words: measurement fusion, distance threshold, active and passive sensors, fuzzy processing, variable-weight

摘要： 针对观测平台和运动对象间的距离参数会对传感器随机测量误差带来影响的问题，提出了一种基于模糊距离阈值的主被动传感器量测融合算法。讨论了根据距离参数选择主被动融合跟踪模式的方法，采用指数函数和模糊处理技术，利用已有信息实时改变主、被动传感器在量测融合过程中所占的权重。仿真结果表明，当传感器和运动对象间的距离对随机测量误差的影响不能忽略时，基于模糊距离阈值的主被动传感器变权重融合算法和传统的固定权重融合算法相比更加稳定，能够充分发挥主、被动传感器间的互补特性。

关键词: 量测融合, 距离阈值, 主被动传感器, 模糊处理, 变权重