摘要
机动目标的运动特性具有复杂性、多变性、无先验性。多传感器信息融合的机动目标跟踪问题可以描述为数据关联和状态估计两个相关功能的最优化问题。本文分析了运用于多传感器数据融合机动目标跟踪的数据关联和状态估计算法,得出了高效费比的模糊数据关联算法(FDA)和变结构多模算法(VSMM)。此外,并分别介绍了模糊数据关联算法和相似模型集的变结构多模算法。将模糊数据关联算法和变结构多模算法结合起来,从而提高对机动目标跟踪的性能。
The motion of maneuvering targets is characterized of complexity, polytropy and non-apriority. The problems of multi-sensor data fusion targets tracking maneuvering can be described as the optimization of two related data association and state estimation functions. This paper analyzes the data association and state estimation algorithms applied in multi-sensor data fusion maneuvering target tracking, come to the conclusion of a high cost-effective fuzzy data association (FDA) algorithm and variable structure multiple model (VSMM) algorithm. Moreover, a FDA algorithm and a likely model-sets VSMM algorithm are introduced respectively. The FDA combined with VSMM will enhance the performance of maneuvering target tracking.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2006年第z2期1679-1682,共4页
Chinese Journal of Scientific Instrument
关键词
机动目标
模糊数据关联
变结构多模算法
多传感器数据融合
maneuvering target fuzzy data association variable structure multiple model multisensor data fusion
