| 摘要: |
| 针对箔条干扰时目标与干扰难以区分的问题，设计了一种基于多特征向量的分类算法。该算法首先对目标和箔条的特征进行分析，而后选择并构造了一组具有较高区分度的极化特征识别量，最后采用支持向量机（SVM）方法，通过对特征样本进行训练，获得了较好的分类结果。实验表明，所提算法具有较强的抗箔条干扰能力，且检测正确率可达90%以上。 |
| 关键词: 多特征目标 雷达回波 抗干扰检测 支持向量机 |
| DOI：10.3969/j.issn.1001-893x.2017.08.007 |
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| 基金项目: |
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| Multi-feature target anti-interference detection based on support vector machine |
| CHEN Jun |
| (Southwest China Institute of Electronic Technology,Chengdu 610036,China) |
| Abstract: |
| Chaff interference can bring many difficulties in target detection. Addressing on this problem, a classification algorithm is proposed in this paper. The multi-features of targets and chaffs are analyzed and extracted firstly.Then, a polarized eigenvector is designed.Finally, support vector machine(SVM) approach is applied and through data training，good classification results are achieved. The simulation results demonstrate that the proposed method has good anti-chaff -interference ability and the correct detection probability is greater than 90% at most time. |
| Key words: multi-feature target radar echo anti-interference detection support vector machine(SVM) |
