雷达字作为构成雷达短语的基元, 其提取效果的好坏将直接影响后续雷达行为辨识的可信度。针对侦收数据不均衡情况下的雷达字提取问题, 提出一种基于K-means算法改进的K-OPTICS雷达字提取算法。通过构建虚拟聚类中心和簇合并的方法, 使其在各种样本不均衡的仿真场景下仍能取得91.22%以上的提取准确率, 较传统算法具有更好的参数不敏感性。

Radar words are the primitives of radar phrase,and the extraction effect of radar words will directly affect the confidence of subsequent radar behaviour recognition.A solution is proposed for the radar word extraction problem in the case of unbalanced detection and acquisition data,i.e.an improved K-OPTICS radar word extraction algorithm based on K-means.By constructing virtual clustering centres and cluster merging,it achieves an extraction accuracy higher than 91.22% under various unbalanced sample simulation scenarios,and has better parameter insensitivity than the traditional algorithm does.