基于FISST的多源异类信息配准算法
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国家自然科学基金NSAF资助项目(U1330133)

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FISST based dissimilar information of multi-source registration algorithm
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    摘要:

    针对超高速、超强机动等特定目标的探测和跟踪问题,雷达等传统传感器的量测值少,难于获得有效的探测跟踪,还需要人工情报、专家库信息等异类信息,对于这些信息的配准融合处理尚无有效方法。基于有限集合统计学(FISST)理论,在对几种异类信息进行统一表示并给出相应的滤波处理方法基础上,提出了焦点目标的匹配概率(匹配度)作为异类信息配准指标,并采用卡尔曼证据滤波方法进行计算。最后给出了仿真实例,验证了方法的有效性和可行性。

    Abstract:

    It is hard to detect and track special targets with super high-speed and super strong- maneuverability efficiently, only by traditional sensors such as radar. Therefore, intelligence information and expertise base etc. should be needed. Nevertheless, there are no practicable means for the management of multi-source heterogeneous information registration. The characteristics of special targets are introduced and analyzed firstly. Some kinds of heterogeneous information are expressed in Finite-Set Statistics(FISST) theory and some corresponding filtering algorithms are put forward. The matching probability of focused targets is taken as the registration index of heterogeneous information, and the results are computed with Kalman evidence filter. Numerical simulation validates the feasibility of the proposed method.

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叶 宏,曹学军,李 军,李世玲,屈新芬,杨战平.基于FISST的多源异类信息配准算法[J].太赫兹科学与电子信息学报,2014,12(6):865~869

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  • 收稿日期:2013-09-23
  • 最后修改日期:2014-03-27
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  • 在线发布日期: 2015-01-05
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