基于目标预测值模糊化的模糊数据关联算法

Fuzzy Data Association Algorithm Based on Fuzzed Target Forecasted Value

对传统模糊数据关联算法进行了分析,针对其利用最大隶属值赋值原则对目标进行跟踪时可能存在误跟或跟错的情况,提出了基于目标预测值模糊化的模糊数据关联算法。首先将预测航迹进行距离模糊化,建立联合关联区并舍去关联区以外的点迹,降低模糊数据关联分割矩阵的维数;然后对分割矩阵运用整体最优原则,将问题转化为0-1规划问题,实现数据的模糊关联;最后对算法进行仿真,结果表明:采用新算法能有效改进传统算法的缺陷,提高数据关联的可靠度。

The traditional method of fuzzy dataassociation algorithm is analyzed. Aimed at false or incorrect target tracking while using the principle of largest subjected value to track target, an arithmetic of fuzzy data association based on fuzzy target predicted value is presented. Firstly, the predicted flight range, distance is made to be fuzzy, the untited associated area is established and the spot existing out of the area is get rid of, which will reduce the number of rows of the divided matrix. Then the principle of holistic best value is used to solve the problem, transform the problem into 0-1 programing and realize the fuzzy association of data. Finally, the new arithmetic is simulated. The results indicate that the improved arithmetic can offset the disadvantages of traditional method and enhance the reliability of the data association.