Frequency-hopping (FH) communication is a pivotal anti-jamming technology, offering robustness in complex electromagnetic environments due to its frequency diversity and random hopping. However, the continuous advancement of jamming techniques increasingly compromises the anti-jamming capabilities of high-speed FH systems, making accurate jamming detection a critical challenge. To address the frequent misclassification of FH signals and noise as jamming, this paper proposes a jamming presence discrimination algorithm based on Silent Period Insertion and Multi-segment Signal Spectrum (SPI-MSS). By introducing silent time slots and combining multi-segment spectral estimation, the method effectively suppresses false alarms caused by noise fluctuations and FH signals. Furthermore, a multi-node collaborative sensing mechanism is incorporated to improve system-wide detection probability through fusion of distributed node results. After confirming the existence of jamming signals, a multi-model clustering-based parameter estimation algorithm is applied, leveraging spectral information across nodes and time segments with adaptive clustering fusion to reduce estimation errors. The proposed approach enhances anti-jamming performance and communication stability in multi-node cooperative operations, especially for clustered FH systems with numerous nodes and complex electromagnetic environments. Simulation results demonstrate that the proposed algorithm effectively reduces the false alarm probability and improves detection performance in complex jamming scenarios, increasing the MTJ detection probability from 35\% to 78\% at JNR=−22 dB; additionally, improved parameter estimation precision is achieved, with a reduced normalized root-mean-square error for MTJ center frequency estimation at JNR=−16 dB, confirming its suitability for practical engineering applications.
Zhao et al. (Mon,) studied this question.