基于时差多参分选的多层感知器网络脉间识别

Recognition of Pulse Repetition Interval of Multilayer Percetron Network Based on Multi-parameter TDOA Sorting

现代战争中,雷达系统发展迅速。为识别复杂的雷达信号调制模式以及混合体制雷达,该文提出一种基于多站获取脉冲时差参数联合其他脉冲描述字分选的办法,利用多层感知器神经网络得到脉间调制识别结果。该文通过时差参数与其他脉冲描述字去交错解决传统脉冲重复周期估计算法无法对复杂的脉间调制方式进行估计。利用训练好的多层感知器,获取完成去交错后的脉冲序列其特征向量,获得其脉间调制类型识别。通过实验仿真,在脉冲丢失率不高于20%情况下,对复杂脉间调制方式的正确识别概率在90%以上。

In modern warfare, the radar system is developing rapidly. To recognize complex modulation mode of radar signal and hybrid pulse repetition interval radar, this paper proposes a sorting method based on multi station acquired pulse time-difference parameter combined with other pulse description words, taking advantage of the multi-station TDOA from the same emitter is similar to sort emitter signal pulse, and finally got the recognition result with the Multi-Layer Percetron （MLP） neural network. Traditional Pulse Repetition Interval （PRI） estimation algorithms estimate complex pulse interval modulation invalidly. In this paper, to solve this problem pulse time-difference parameter and other pulse description words are used. The feature vector of de-interlace pulse sequence is acquired and the result of pulse interval modulation type recognition is obtained with the trained MLP neutral network. Through experimental simulation, the correct recognition probability of the complex pulse interval modulation method is more than 90% in the case of the pulse loss rate is not more than 20%.