The kernel based weighted KNN algorithm solves the multi peak distribution problem and the overlap boundary problem of the sample set, as well as the classifier's precise decision problem.
基于核的距离加权KNN算法解决了样本的多峰分布、边界重叠问题和分类器的精确分类决策问题。
Smart noise jamming waveforms of pulse-overlap and pulse-sample are designed. And The PC simulation results validate that the two smart noise jamming waveforms are effective.
并以线性调频雷达为干扰对象，将脉冲重叠型和脉冲等间隔取样两种灵巧噪声干扰波形的干扰效果进行了计算机仿真，仿真结果验证了这种干扰的有效性。
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