以此为前提，提出了距离阈值法和向量角度法相结合的数据精简算法。
Under this premise, the algorithm of data simplification is offered which combines the method of distance threshold with the one of vector angle.
由目标的实测数据，计算特征向量总体的均值和协方差矩阵及其逆矩阵，由距离判别法进行目标识别。
The mean and covariance matrixes and their inverse matrixes of all eigen vectors are obtained through measured data of the target. And the target is recognized by techniques of range discrimination.
采用随机键，将连续的粒子位置向量转化为离散的解向量，并通过提出相对最短距离法来评价解集的优劣。
The random key was adopted to change from continuous particle position vectors to discrete solution vectors. And the method of relatively minimum distance was proposed to evaluate the Pareto muster.
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