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

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 -