本文研究了无杂波和漏检的情况下三种不同类型、不同位置的传感器对数目未知的目标进行检测时的静态数据关联问题.这一问题可以通过对测量划分的联合似然函数的极大化来解决,通常可将其转化为三维匹配问题,但其求解的复杂度是NP的.本文提出了一种基于遗传算法的优化算法,来解决三维匹配问题,实验结果表明这种算法具有很高的关联成功率. In this paper, we study the static data association problem of unknown number of targets in three different types and locations of sensor without clutter and missed detection, which can be solved by measuring the pole of joint likelihood function It can usually be transformed into a three-dimensional matching problem, but the complexity of the solution is NP. In this paper, an optimization algorithm based on genetic algorithm is proposed to solve the three-dimensional matching problem. The experimental results show that this algorithm has High correlation success rate.