| The modern information warfare is characterized by various combat modes,diversified combat objects and complicated and changeable battlefield environment.This requires the modern C4 ISR system to quickly and accurately integrate a large number of diverse heterogeneous data information to provide evidence for combat decision support.Therefore,situation assessment in battlefield has become a challenging problem in the multi-sensor information fusion field.In practical application,how to fuse a small amount of important conflict information;how to make a consistent evaluation results;how to solve the redundant data problems arising when a large number of data fusion problems,Which is the difficult problem to be solved urgently in the current situation assessment research.In order to solve the problem described above,this thesis studies the robust fusion methods for conflict data.The main contents are as follows:Firstly,the background and significance of this research are briefly introduced,at the same time,the status quo of the research on situation assessment and its consistency are reviewed.Then,the cause of the uncertainty information problem in the process of situation estimation is discussed in detail,and introduces some typical methods to deal with the uncertainties in the situation assessment,Which lays the foundation for the study of situation assessment.Secondly,the estimating results of traditional harmonic situation assessment methods will degrade when fusing multi-source conflict data.To this end,a new method of non-harmonic situation assessment is proposed based on conflicting data clustering in this chapter.First,an iterative self-organizing data clustering method is used to cluster the multi-source conflict data.Then,the importance of the data clusters is evaluated by its frequency and reliability.Finally the situation assessment results are achieved.The simulation results show that: compared with the traditional D-S evidences reasoning method,the proposed method can obtain higher confidence fusion results from multi-source conflict data.Thirdly,the heterogeneous sensors may produce a large amount of redundant and conflicting information,which leads to the problem of the degradation of the conventional situation estimation method,the fourth chapter puts forward a robust situation estimation method based on heterogeneous sensor information.First,a large number of sensors are divided into two groups-all-weather and auxiliary sensors,Then,the two-level fusion structure is used to integrate all-weather sensor information,and the fusion result is evaluated based on Jousselme distance.Finally,the sensor reliability is adjusted adaptively based on the current situation estimation results.The simulation results show that: the method proposed in this paper can improve robustness of the situation estimation results on the basis of efficiency.Finally,the main work and further research of the thesis is summarized. |
