基于多维熵与优化SVM的调频引信抗干扰方法

An anti-jamming method for FM fuzes based on multidimensional entropy and optimized SVM

关键词:  无线电引信  信息型干扰  频域熵特征  鹦鹉优化算法  支持向量机

Key words:  radio fuze  information jamming  frequency-domain entropy features  parrot optimization algorithm  support vector machine

摘要:

为解决调频无线电引信在复杂电磁环境中易受调幅扫频式信息型干扰威胁问题,本文提出一种基于频域熵特征与鹦鹉优化算法（parrot optimization algorithm,POA）优化支持向量机（support vector machine,SVM）的分类抗干扰方法。首先,利用快速傅里叶变换将引信检波端输出信号从时域转换至频域,计算频域信息熵、指数熵和R范数熵,构建三维特征矩阵；而后采用POA优化SVM分类器参数,优化后的SVM采用高斯核函数,其惩罚参数C和高斯核参数σ通过POA自适应调整,以提升模型分类性能。实验结果表明,目标与典型干扰（噪声、正弦、方波调幅扫频）的熵特征在概率密度分布上具有显著可分性； POA在300次迭代内快速逼近最优解,适应度值稳定在0.001以内。经微波暗室验证,POA-SVM的目标识别准确率达96.8%,干扰识别准确率为97.2%,较传统SVM与PSO-SVM方法均有显著提升。经Modelsim仿真验证,算法响应性能满足毫秒级引信工作要求。本文方法能够有效提升无线电引信对信息型干扰的识别精度与实时性,可为复杂电磁环境下引信抗干扰识别提供一种新途径。

Abstract:

To address the vulnerability of frequency-modulated (FM) radio fuzes to amplitude-modulated sweep-frequency information-based jamming threats in complex electromagnetic environments, this paper proposes a classification anti-jamming method based on frequency-domain entropy features and a parrot optimization algorithm (POA) optimized support vector machine (SVM). First, the output signal of the fuze detector stage is transformed from time domain to the frequency domain using the fast Fourier transform (FFT). Three entropy measures — frequency-domain information entropy, exponential entropy, and R-norm entropy are then calculated to construct a three-dimensional feature matrix. Subsequently, the POA is employed to optimize the parameters of SVM classifier. The optimized SVM utilizes a Gaussian kernel function, with its penalty parameter C and Gaussian kernel parameter σ adaptively adjusted by the POA to enhance the classification merit. Experimental results demonstrate that the entropy features of the target and typical interferences (noise, sine wave, square wave amplitude modulation sweep) exhibit significant separability in their probability density distributions. The POA rapidly converged to the optimal solution within 300 iterations, with fitness values stabilizing below 0.001. Validation in a microwave anechoic chamber confirmed that the POA-SVM achieved 96.8% target recognition accuracy and 97.2% interference recognition accuracy, representing significant improvements over traditional SVM and PSO-SVM methods. Furthermore, Modelsim simulations confirmed the algorithms response performance meets millisecond-level operational requirements of fuzes. The proposed approach effectively enhances both recognition accuracy and real-time capability of FM radio fuzes against informational jamming, offering a novel pathway for fuze anti-jamming recognition in complex electromagnetic environments.