Smart noise jamming suppression method based on fast fractional filtering

Han, Bowen; Yang, Xiaopeng; Wu, Xuchen; Li, Shuai

doi:10.1049/joe.2019.0270

Cited by 8 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, in smart noise product jamming, the radar transmits the signal, and the noise signal is multiplied, modulated, and amplified to be ready for transmission. In contrast to smart noise convolution jamming, the noise signal is a Gaussian white noise after applying the low-pass filtering [30,31].…”

Section: Signal Data Acquisitionmentioning

confidence: 99%

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

Jiang,

Yang,

Zhang

et al. 2024

IET Signal Processing

View full text Add to dashboard Cite

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.

show abstract

Section: Signal Data Acquisitionmentioning

confidence: 99%

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

Jiang,

Yang,

Zhang

et al. 2024

IET Signal Processing

View full text Add to dashboard Cite

show abstract

“…However, this method can only handle jamming with lower jamming-to-signal ratios (JSR) and cannot accurately obtain jamming parameters when the jamming JSR is high and suppressive. In Reference [20], a smart noise suppression method for fast fractional filtering was proposed. This method performs a fractional Fourier transform (FrFT) on the signal, selects the optimal fractional transform rotation angle, extracts target parameters based on the fractional fourth order origin moment, and designs a filter in the fractional domain to filter out jamming.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Characteristics and Suppression Methods for Self-Defense Smart Noise Jamming

Zhu,

Zhang,

et al. 2023

Electronics

View full text Add to dashboard Cite

Self-defense smart noise jamming can automatically aim a signal frequency and obtain antenna gain and matching filtering processing gain, posing a huge threat to the normal performance of radar. In response to this situation, this article conducts an in-depth analysis of two typical smart noise jamming methods: noise convolution jamming methods and noise product jamming methods. The distribution characteristics of the two jamming methods in both time–frequency dimensions and their internal time–frequency relationships are analyzed. Based on this, a self-defense smart noise jamming suppression method based on pulse frequency stepping is proposed. This method obtains the true distance information of the target based on the phase difference caused by frequency stepping between adjacent pulses and uses this information to construct a filter to filter the radar echo, achieving jamming suppression. Simulation experiments have verified the effectiveness of this method.

show abstract