基于PSO-CNN的LPI雷达波形识别算法

低截获概率(low probability of intercept, LPI)雷达作为一种具有强抗干扰能力及低截获特性的新型雷达, 对其精准高效识别已成为雷达对抗一方波形识别的难点。针对该方向主流分类器卷积神经网络(convolution neural network, CNN)的结构智能寻优问题, 提出一种基于粒子群优化(particle swarm optimization, PSO)算法-CNN的波形识别算法。该算法利用PSO的寻优特性, 可实现较大范围内自动搭建不定层数、不定层类别及层内参数的CNN结构并进行迭代寻优; 采用识别精度及网络复杂度相结合的衡量指标, 可根据需求调整两者比重以实现对精度与轻量性的选择。该算法获取的CNN结构实现了比9种经典CNN结构更好的LPI雷达波形识别效果, 同时避免了波形识别时人工选定CNN超参数缺乏智能性、客观性的问题, 提高了选用CNN结构的适配性及高效性。

LPI radar waveform recognition algorithm based on PSO-CNN

Low probability of intercept (LPI) radar is a new type of radar with strong anti-jamming ability and low interception characteristics. Accurate and efficient recognition of it has become a difficult problem in waveform recognition of radar confrontation. Aiming at the structural intelligent optimization problem of the mainstream classifier convolution neural network (CNN) in this direction, a waveform recognition algorithm based on particle swarm optimization (PSO)-CNN is proposed. The algorithm uses the optimization characteristics of PSO to automatically build CNN structure with indefinite layers, layer types and layer parameters in a large range, evaluate and iteratively optimize it. The algorithm uses a combination of recognition accuracy and network complexity to measure indicators, the proportion of the two can be adjusted according to requirements to achieve the choice of accuracy and lightness. The CNN structure obtained by the algorithm achieves better LPI radar waveform recognition effect than nine classic CNN structures. And the algorithm avoids the lack of intelligence and objectivity of artificially selecting CNN hyper parameters during radar waveform recognition, and improves the adaptability and efficiency of the selecting CNN structure.