基于多维熵与优化 SVM 的调频引信抗干扰方法

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.