基于模拟退火算法的空中目标识别

Air Target Recognition Based on Simulated Annealing Algorithm

根据NMI特征和Jan Flusser提出的仿射不变矩的特点及各自的适用条件,提出了一种组合不变矩。利用所提取的组合不变矩的特征向量,实现了对空中目标的K-mean聚类分析识别。在此基础上将模拟退火机制引入其中,以克服K-mean聚类的局限性和对初始聚类中心的敏感性,最后提出了基于模拟退火的改进K-mean聚类算法。仿真实验结果表明,该方法具有较高的识别精度和较好的抗噪性。

A new combined invariant moment is presented in this paper according to the characteristics of NMI and affine invariant moments proposed by Jan Flusser with their application conditions. Combined invariant moments are extracted as the feature vector, the air target classification based on K -means clustering analysis is implemented successfully. On the basis of systematic analysis of current algorithms, simulated annealing mechanism is inducted into K-means clustering to solve its locality and the sensitive- ness of the initial condition. Then, this paper proposes the improved K -means clustering based on simulated annealing algorithm. The simulation test results show that the proposed method achieves good classification and antinoise performance.