A Smart Noise Jamming Suppression Method Based on Atomic Dictionary Parameter Optimization Decomposition

Liu, Zhidong; Zhang, Qun; Li, Kaiming

doi:10.3390/rs14081921

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…In smart noise convolution jamming, the jammer receives the radar signal and performs a convolution modulation on both the noise and the radar transmission signal. Thus, the signal is amplified and transmitted [29]. The jammer receives the radar signal with a time delay τ; moreover, the Gaussian white noise generated by the jammer is presented by n 1 ðtÞ: .…”

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

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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.

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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

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“…They adopted SVM to achieve classification into smart noise interference and real echos. Li [8] first performed a double orthogonal Fourier transform on the signals, extracted the peak slope and set its threshold. After dividing the frequency spectrum diffusion interference, slice reconstruction interference, noise multiplication interference, noise convolution interference, intermittent sampling forwarding interference and real echo into three groups, five-dimensional features such as spectral entropy and cross-correlation coefficients were extracted and input into a trained support vector machine to complete the recognition and classification.…”

Section: Related Workmentioning

confidence: 99%

Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference

Wu,

Zhou,

et al. 2023

Sensors

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In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4–9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is −6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.

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“…Noise blanket jamming can be implemented in the form of broadband, narrowband, or pulse noise with short-term high-power characteristics. This jamming type causes the radar receiver to saturate within its dynamic range, thereby reducing the radar’s target detection ability [ 19 , 20 , 21 , 22 ]. Periodic modulation interference includes active jamming from the jammer and mutual interference from other radars.…”

Section: Introductionmentioning

confidence: 99%

An Interference Mitigation Method for FMCW Radar Based on Time–Frequency Distribution and Dual-Domain Fusion Filtering

Zhou,

Cao,

Zhang

et al. 2024

Sensors

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Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar’s beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.

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A Smart Noise Jamming Suppression Method Based on Atomic Dictionary Parameter Optimization Decomposition

Cited by 9 publications

References 23 publications

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

Deep Learning-Based Active Jamming Suppression for Radar Main Lobe

Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference

An Interference Mitigation Method for FMCW Radar Based on Time–Frequency Distribution and Dual-Domain Fusion Filtering

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