基于自适应α截集特征提取的雷达杂波识别

Radar Clutter Recognition Based on Feature Extraction by Adaptive- a Truncation Set

α截集特征提取雷达杂波识别方法所需样本数目少,识别率高,在雷达杂波识别领域具有重要应用。通常α截集特征提取方法中的α值是主观给出的一个小数,在多变的雷达杂波环境中不能使α截集特征提取方法给出最佳的识别效果。本文提出了一种α值自适应选取的α截集雷达杂波识别方法,建立了“各可能分布的α截集值差异程度”与α值间的对应关系,并利用“差异程度最大”准则对α值进行优化,克服了上述缺点,实现了算法对杂波环境的自适应。仿真结果表明,本方法能够提供对雷达杂波更高的识别率,是一种更有效、更具实用意义的雷达杂波识别方法。

Compared to other methods, the feature extraction of α truncation set needs fewer samples and has higher recognition rate, which plays an important role in radar clutter recognition. Usually, the value of α in this method is a subjectively given decimal, and can not offer optimal recognition results in varied clutter environment. A new α truncation set feature extraction based method with adaptive selection of α is proposed. The corresponding relation between α and “the difference among all possible distributions” is established and the value of α is optimized by the “difference maximization” criterion. Our method can adapt to different clutter environment, which overcomes the shortcoming above. Simulation results show that this new method has a higher recognition rote, and is more valid, practical and prospective in radar clutter recognition.