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针对运动目标 Ｒ Ｃ Ｓ 变化规律，提出将目标 Ｒ Ｃ Ｓ 变化序列分解为确定变化序列（ Ｃｅｒｔａｉｎ Ｖａｒｉａｔｉｏｎ Ｓｅｑｕｅｎｃｅ， Ｃ Ｖ Ｓ） 和随机变化序列（ Ｒａｎｄｏｍ Ｖａｒｉａｔｉｏｎ Ｓｅｑｕｅｎｃｅ ， Ｒ Ｖ Ｓ） 的设想．其中 Ｃ Ｖ Ｓ 敏感于姿态，在姿态域表现为丰富的细节特征，而 Ｒ Ｖ Ｓ则表现为近似正态分布的非平稳随机过程．本文用实验数据证明这两种变化序列均具有分形的特征．由以上的分析结果，利用分形迭代函数系统将这两种变化序列进行有效分解，其分解效果明显优于传统的滤波方法．
Aiming at the variation rule of R C S, this paper proposes the idea of decomposing the target R C S variation sequence into a Certain Variation Sequence (C V S) and a Random Variation Sequence (R V S). Among them, C V S is sensitive to attitude and rich in detail in attitude field, while R V S is a non-stationary stochastic process with approximate normal distribution. In this paper, experimental data prove that these two kinds of change sequences all have fractal characteristics. From the above analysis results, the fractal iterative function system is used to effectively decompose these two change sequences, and the decomposition effect is obviously better than the traditional filtering method.