基于三级信息融合结构的多平台多雷达目标识别算法

An Effective Multi-Platform Multi-Radar Target Identification Algorithm Based on Three Level Fusion Hierarchical Structure

为了解决多平台多雷达目标识别时空信息融合过程中存在的时效性不高,识别特征信息模糊和雷达识别结果高冲突等问题,提出了一种基于三级信息融合结构的多平台多雷达目标识别算法。一级特征融合中,基于模糊评判法解决了多特征识别信息模糊问题。二级空域融合中,基于DS融合规则和折扣融合规则,提出了以平均证据距离为阈值的自适应证据融合算法。三级时域融合基于多雷达序列信息,给出了时域自适应融合算法。仿真计算结果表明文中提出的融合结构能够在保证结果正确的同时,每个周期都能输出结果,时效性较之以往的融合结构有了明显改善。文中的自适应证据融合算法能够克服证据间可能存在的高冲突性,并且与采用折扣证据融合算法相比计算量显著减少。

There are some factors that influence the multi-radar target identification time-space information fusion： the low timeliness, the high conflicts among identification results of sensors, and the fuzziness and the uncertainties of identification features. Sections 1 and 2 of the full paper explain the algorithm mentioned in the tide, which we believe is effective and its core consists of： ＂ Section 1 designs three level fusion hierarchical structure for multi- platform multi-radar target identification. Subsection 1.1 uses the fuzzy synthetic evaluation to describe the fuzzi- ness and uncertainties of identification features to get the identification results of single radar. Subsection 1.2 de- signs the algorithm that adaptively chooses evidence fusion rules for spatial information fusion, according to the threshold of the average evidence distance. Subsection 1.3 proposes the adaptive time evidence fusion algorithm, based on sequence information of multi-radar. Subsection 2. 1 explains simulation calculations. ＂ Simulation re- sults, presented in Tables 2 through 5, and their analysis show preliminarily that： （1） our fusion hierarchical structure can indeed get the right output result for each period, and its timeliness is better than that of Ref. 3 by Hong and Lynch; （2） Tables 2 and 3 show that our adaptive evidence fusion algorithm, compared with DS fusion rules and discount evidence fusion rules, can indeed not only solve the problem of the high conflicts among identifi- cation results of sensors but also reduce the computational complexity.