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目前大多数据融合算法只考虑那些与目标状态向量直接相关的运动学信息如距离,角度信息等,没有考虑目标的参数信息。因此用于融合的信息少、质量低,从而引起复杂环境下关联正确率低。实际上,传感器不只获得这些信息,还可获得更多的目标的其它参数信息。因此可以结合不同传感器的多种信息提高数据关联精度和可靠性。本文研究多参数目标的信息融合算法。
At present, most data fusion algorithms consider only those kinematics information directly related to the target state vector such as distance and angle information, without considering the target parameter information. As a result, there is less information to be used for convergence and low quality, resulting in a low accuracy of association in a complex environment. In fact, the sensor not only obtains this information, but also gets more information about other target parameters. Therefore, a variety of sensors can be combined with a variety of information to improve data correlation accuracy and reliability. This paper studies the multi-parameter target information fusion algorithm.