In response to the problems of “premature explosion” and “misfire” caused by sweeping jamming in frequency modulated fuzes, a jamming elimination and signal reconstruction method combining constant false alarm rate (CFAR) detection and Burg extrapolation is proposed. To eliminate the impact of jamming on the fuzes, the technique uses a CFAR detector to detect the time-frequency distribution of the jamming and sets the signal energy of the jamming position to zero. This is based on the distribution differences between the target echo signal and the jamming of the beat signal in the time-frequency domain. A Burg extrapolation-based signal reconstruction technique is suggested to rebuild the target echo during the zeroing interval, aiming to address issues like partial fragment loss and the decrease in signal-to-noise ratio brought on by zeroing. The performance of the method is verified by simulation and actual measurement. The outcomes demonstrate that the anti-jamming method put forth in this research is capable of successfully suppressing sweep interference in the frequency modulated fuze beat signal.

Frequency modulated continuous wave (FMCW) Doppler fuze is easy to be interfered with on the battlefield, resulting in an early explosion and loss of damage ability. To improve the anti-jamming ability of FMCW Doppler fuze against information-based jamming and realize the distinction between multiple jamming signals and target echoes, this paper proposed a method of target and jamming signal classification and recognition based on supervised contrastive learning. Firstly, the backbone network was constructed by residual network and self-attention mechanism. Then, the contrastive learning loss function was improved by introducing labels, and supervised contrastive learning was realized. Finally, an intermediate frequency signal was used to build the dataset, and the network was trained by supervised comparative learning, so as to realize the classification and recognition of the target and jamming signal. The simulation results show that this method can realize the recognition of multiple jamming types and target echoes, and the recognition rate can reach 98.7%. In the low signal-to-noise ratio (SNR) environment, the recognition effect is better. In the SNR environment of −18 dB, the recognition rate is still 91.81%, which is higher than the 86.12% recognition rate of ordinary residual networks.

An anti-sweep jamming method for frequency modulation (FM) fuze based on outlier reconstruction was proposed for the problem of FM fuze against sweep jamming. Based on the iterative filtering decomposition (IFD) algorithm, the intermediate frequency signal of fuze was decomposed into intrinsic mode function (IMF) components. Sweep jamming was represented as outliers in the IMF components. By detecting and eliminating outliers in the IMF components and using a long short-term memory (LSTM) network to build a data reconstruction network model, the data of outliers was reconstructed, and the sweep jamming could be suppressed. The effectiveness of the proposed method was verified by simulation. The results show that the proposed method improves the ability of FM fuze against sweep jamming.

To enhance the frequency modulation radio fuze’s ability to prevent interference in the complex electromagnetic environment, a method of classifying and identifying the target and interference signals is suggested. This method is based on the sparse representation theory and uses the sparse representation coefficient reconstruction. The goal is to address the issue of the frequency modulation radio fuze’s insufficient ability to prevent interference. The output signals of the detector end of the radio fuze were collected, and the overcomplete dictionary of the target signal and the overcomplete dictionary of the interference signal were constructed. The test signals underwent sparse decomposition and reconstruction on both types of dictionaries. Based on the reconstruction errors, the test sample categories were determined. The results show that the classification and recognition method of frequency modulation radio fuze targets and interference signals based on sparse representation can effectively recognize targets and interference signals, and at the same time meet the lower false alarm probability. The research results have important reference significance for frequency modulation radio fuze’s anti-jamming and effective attack in complex electromagnetic environments.