面向多源传感器信息的态势估计方法研究

发布时间：2018-06-05 00:55

本文选题：信息融合 + 态势估计　；参考：《杭州电子科技大学》2017年硕士论文

【摘要】：现代信息战争中呈现出多样化的作战方式、多元化的作战对象以及复杂多变的作战环境等特点。这就要求现代C4ISR系统能快速准确的融合大量多元异构的战场数据信息,为形成作战决策提供证据支持。因此,战场中的态势估计研究成为多传感器信息融合领域的一个重要课题。在实际应用中,如何对多传感器提供的大量数据信息作出相关性分析;如何发现并利用少量但重要的冲突信息;如何对融合后态势结果做出一致性评价;如何解决融合时大量数据信息会出现的冗余问题,是目前态势估计研究亟需解决的若干难点和关键问题。针对上述问题,本文针对态势估计中冲突数据信息,以提高融合结果的鲁棒性及可信度为目标开展研究。主要的研究内容如下:首先,简述了本课题研究的背景和意义,以及对态势估计及其一致性的国内外研究现状进行综述。然后详细讨论了在态势估计过程中导致不确定性信息问题出现的原因,并介绍了处理态势估计中不确定性问题的一些典型方法,为下文对态势估计问题的研究奠定基础。其次,针对基于D-S证据理论的态势估计方法在处理多源冲突数据时融合效果不佳的问题,第3章提出了一种基于冲突数据聚类的态势估计方法。首先结合Jousselme距离和传统冲突系数构建一种新的冲突证据表征方式;然后利用迭代自组织数据聚类方法对数据进行聚类;最后对不同聚类簇的证据采用D-S理论融合得到态势结果,同时构建距离准则函数评价态势结果的一致性。仿真结果表明:与传统态势估计方法相比,本文所提方法在融合多源冲突数据时能够得到可信度较高的态势估计结果。再次,异类传感器产生大量冗余、冲突的信息,导致常规态势估计方法性能下降的问题,第4章提出基于异类传感器信息自适应融合的鲁棒态势估计方法。首先将大量传感器分为两组——全天候和辅助型传感器;然后构造两级融合结构,并基于Jousselme距离评估融合结果的一致性;最后在此基础上自适应地通过两级信息融合得到态势估计结果。仿真结果表明,本文所提方法能够在兼顾运算效率的基础上提高态势估计结果的鲁棒性。最后,对本文所研究的问题进行了总结与展望。
[Abstract]:In modern information war, there are many characteristics, such as diversified combat mode, diversified combat object and complex and changeable combat environment. This requires that modern C4ISR systems can quickly and accurately integrate a large number of heterogeneous battlefield data information and provide evidence support for the formation of operational decisions. Therefore, the research of situation estimation in battlefield becomes an important subject in the field of multi-sensor information fusion. In the practical application, how to make the correlation analysis to the massive data information provided by the multi-sensor, how to find and utilize the small amount of but important conflict information, how to make the consistency evaluation to the result of the fusion situation; How to solve the redundant problem caused by a large amount of data information in fusion is a difficult and key problem that needs to be solved in the research of situation estimation at present. In order to improve the robustness and reliability of fusion results, this paper aims to improve the robustness and credibility of fusion results by focusing on the conflict data information in situation estimation. The main research contents are as follows: firstly, the background and significance of this research are briefly introduced, and the current research situation of situation estimation and its consistency is summarized at home and abroad. Then, the causes of uncertain information problems in the process of situation estimation are discussed in detail, and some typical methods to deal with the uncertainty problems in situation estimation are introduced, which will lay a foundation for the research of situation estimation problems below. Secondly, aiming at the problem that the situation estimation method based on D-S evidence theory is not effective in dealing with multi-source conflict data, chapter 3 proposes a situation estimation method based on conflict data clustering. Firstly, a new representation of conflict evidence is constructed by combining Jousselme distance and traditional conflict coefficient. Then, iterative self-organizing data clustering method is used to cluster the data. Finally, D-S theory is used to fuse the evidence of different clusters to obtain the results. At the same time, the distance criterion function is constructed to evaluate the consistency of situation results. The simulation results show that compared with the traditional situation estimation method, the proposed method can obtain a more reliable situation estimation result when the multi-source conflict data are fused. Thirdly, heterogeneous sensors produce a lot of redundant and conflicting information, which leads to the performance degradation of conventional situation estimation methods. In chapter 4, a robust situation estimation method based on adaptive fusion of heterogeneous sensor information is proposed. Firstly, a large number of sensors are divided into two groups-all-weather and auxiliary sensors, and then two levels of fusion structure are constructed, and the consistency of fusion results is evaluated based on Jousselme distance. Finally, the situation estimation results are obtained by adaptive two-level information fusion. The simulation results show that the proposed method can improve the robustness of the situation estimation results on the basis of taking into account the computational efficiency. Finally, the problems studied in this paper are summarized and prospected.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：E86;TP212

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