基于狼群算法的GNSS欺骗干扰识别（英文）

发布时间：2018-04-17 07:43

本文选题：全球卫星导航系统 + 欺骗干扰检测　；参考：《Journal of Southeast University(English Edition)》2017年01期

【摘要】：为解决卫星导航接收机在受到欺骗干扰时难以识别欺骗干扰这一问题,提出了一种基于模型的欺骗干扰识别方法.首先将干扰机/卫星发射机以及通信信道建模为Hammerstein模型,然后使用一种新的模型辨识方法——狼群算法来进行模型参数辨识.将估计得到的模型参数作为特征参数,使用欧氏距离比较法实现欺骗干扰的识别.仿真实验验证了所提方法的有效性和鲁棒性.结果表明:狼群算法与最小二乘法、迭代法和蝙蝠算法等其他模型辨识算法相比,虽然在算法时间复杂度上比最小二乘法和蝙蝠算法略高,但具有更高的模型参数辨识精度和欺骗干扰识别率,甚至在信噪比较低时识别性能也最优.
[Abstract]:In order to solve the problem that it is difficult for the satellite navigation receiver to identify the deception jamming when the receiver is deceived, a model-based deception jamming identification method is proposed.Firstly, the jammer / satellite transmitter and communication channel are modeled as Hammerstein model, and then a new model identification method-wolf swarm algorithm is used to identify the model parameters.Euclidean distance comparison method is used to identify deception interference by using the estimated model parameters as feature parameters.Simulation results show that the proposed method is effective and robust.The results show that compared with other model identification algorithms, such as least square method, iterative method and bat algorithm, the time complexity of the algorithm is slightly higher than that of the least square method and bat algorithm.But it has higher model parameter identification accuracy and deceptive interference recognition rate, and the performance is optimal even when the SNR is low.
【作者单位】：东南大学信息科学与工程学院;杭州电子科技大学通信工程学院;
【基金】：The National Natural Science Foundation of China(No.61271214,61471152) the Postdoctoral Science Foundation of Jiangsu Province(No.1402023C) the Natural Science Foundation of Zhejiang Province(No.LZ14F010003)
【分类号】：TN967.1;TP18

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