TY - JOUR
T1 - Anti-sweep Jamming Method for Frequency-modulated Fuze Based on Sparse Bayesian Learning
AU - Wei, Mingyu
AU - Hao, Xinhong
AU - Yang, Jin
AU - Zhou, Wen
AU - Yang, Qiuyang
N1 - Publisher Copyright:
© 2026, China Ordnance Industry Corporation. All rights reserved.
PY - 2026
Y1 - 2026
N2 - 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 highintensity interference pulses in the time-domain differential frequency signal are accurately located and zero out using the SumThreshold algorithm, thereby fundamentally eliminating the jamming ˊs 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 twodimensional 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.
AB - 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 highintensity interference pulses in the time-domain differential frequency signal are accurately located and zero out using the SumThreshold algorithm, thereby fundamentally eliminating the jamming ˊs 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 twodimensional 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.
KW - antijamming
KW - frequency sweeping jamming
KW - frequency-modulated fuze
KW - sparse Bayesian learning
UR - https://www.scopus.com/pages/publications/105033084762
U2 - 10.12382/bgxb.2024.1148
DO - 10.12382/bgxb.2024.1148
M3 - Article
AN - SCOPUS:105033084762
SN - 1000-1093
VL - 47
JO - Binggong Xuebao/Acta Armamentarii
JF - Binggong Xuebao/Acta Armamentarii
IS - 2
M1 - 241148
ER -