Anti-sweep Jamming Method for Frequency-modulated Fuze Based on Sparse Bayesian Learning

WEI Mingyu; HAO Xinhong; YANG Jin; ZHOU Wen; YANG Qiuyang

doi:10.12382/bgxb.2024.1148

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Anti-sweep Jamming Method for Frequency-modulated Fuze Based on Sparse Bayesian Learning

更新时间：2026-04-24

- Anti-sweep Jamming Method for Frequency-modulated Fuze Based on Sparse Bayesian Learning
- Acta Armamentarii Vol. 47, Issue 2, Pages: 241148(2026)
- 作者机构：
  
  北京理工大学机电动态控制重点实验室，北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.1148
  CLC： TJ301
- Received：25 December 2024，
  
  Online First：25 December 2025，
  
  Published：28 February 2026
- 稿件说明：
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WEI Mingyu, HAO Xinhong, YANG Jin, et al. Anti-sweep Jamming Method for Frequency-modulated Fuze Based on Sparse Bayesian Learning[J]. Acta Armamentarii, 2026, 47(2): 241148.
DOI：

WEI Mingyu, HAO Xinhong, YANG Jin, et al. Anti-sweep Jamming Method for Frequency-modulated Fuze Based on Sparse Bayesian Learning[J]. Acta Armamentarii, 2026, 47(2): 241148. DOI： 10.12382/bgxb.2024.1148.

摘要

连续波调频引信在复杂电磁环境中抗扫频干扰方面存在不足，为此提出一种结合时域干扰剔除与稀疏信号重构的抗干扰新思路。首先利用Sumthreshold算法精准定位并置零时域差频信号中的高强度干扰脉冲，从根本上消除干扰对测距的影响；进而，针对干扰剔除后信号缺失所引发的稀疏性问题，引入稀疏贝叶斯学习算法，通过建立贝叶斯推理模型并优化超参数估计，高效重构目标二维矩阵，从而克服速度测量模糊。仿真和实测实验结果表明，该方法鲁棒性极强，基于稀疏贝叶斯学习的抗扫频干扰方法能够在低信噪比，高样本置零率的条件下，依然能准确恢复目标距离与速度信息，其峰值旁瓣比优于多种现有主流算法，显著提升了引信在恶劣电磁环境下的探测可靠性与抗干扰性能。

Abstract

The frequency-modulated continuous-wave(FMCW)fuzes exhibit deficiencies in anti-sweep jamming in complex electromagnetic environment. To address this issue

an anti-jamming method integrating time-domain interference excision and sparse signal reconstruction is proposed. The high-intensity interference pulses in the time-domain differential frequency signal are accurately located and zero out using the SumThreshold algorithm

thereby fundamentally eliminating the jammingˊs impact on ranging. Subsequently

the sparse Bayesian learning(SBL)algorithm is introduced to tackle the sparsity issue arising from signal loss post-excision. SBL algorithm is used to efficiently reconstruct the target two-dimensional matrix by establishing a Bayesian inference model and optimizing the hyperparameter estimation

thus overcoming the velocity measurement ambiguity. The simulated and measured resukts demonstrate that the SBL-based anti-sweep jamming method has strong robustness. It can accurately recover the target range and velocity information even under the conditions of low signal-to-noise ratio and high sample zero-setting rate. Furthermore

The peak-to-sidelobe ratio(PSLR)achieved with this method is superior to those of several existing mainstream algorithms

significantly enhancing the detection reliability and anti-jamming performance of fuze in harsh electromagnetic environments.

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