关键词: 箔条干扰, 实测数据分析, 特征提取, 目标识别, 机器学习

Abstract:

A typical passive interference measure, the chaff interference, has a complex diffusion process and changeable characteristic information. The existing countermeasures do not take both the accuracy and the universality into account. To address this problem, a more effective anti-chaff jamming method based on the distance and frequency distribution features of the range-Doppler (R-D) diagram is proposed. Firstly, a mean-shift clustering algorithm to separate the point sets of target and chaff is employed, and then new feature information such as frequency offset sum and other new features to assist the machine learning classifiers in completing the confrontation recognition of the diffusion process is extracted. Finally, the proposed method is applied to a large amount of anti-chaff jamming real-life data of a certain coherent terminal guidance radar. The data processing results show the changes of each feature of the jamming during the whole process of chaff bombs from firing to complete spreading, and based on this, discuss the stability of the features and the reliability of the anti-jamming method. Both the theoretical analysis and the measured data processing results show that the proposed anti-chaff jamming method can accurately recognize the chaff during the whole diffusion process, and therefore the proposed method has remarkable anti-jamming performance and good environmental adaptability.

Key words: chaff jamming, measured data analysis, feature extraction, target recognition, machine learning