An Antideceptive Jamming Method for Multistatic Synthetic Aperture Radar Based on Collaborative Localization and Spatial Suppression

Wang, Wenjing; Wu, Junjie; Pei, Jifang; Mao, Xinyu; Yang, Jianyu

doi:10.1109/jstars.2020.2997345

Cited by 19 publications

(3 citation statements)

References 45 publications

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“…R ADAR can obtain long-range target information in allweather and real-time to achieve target detection, tracking, or imaging, which plays an irreplaceable role in military and civil fields [1], [2], [3], [4]. However, with the rapid development of electronic warfare (EW) technology, more advanced radar jamming measures have severely impaired radar detection performance [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

Quan

Sha³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional antijamming methods usually need to estimate the jamming parameters and have the risk of losing target information. For the above problems, this article proposes a deep neural network-based ISRJ recognition and antijamming target detection method which consists of four serial steps. First, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Second, a youonly-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Third, the anti-ISRJ target ranging and velocity measurement datasets are constructed according to the positioning result. Finally, an anti-ISRJ target detection model based on the convolution neural network (CNN) is designed to extract features along different dimensions and obtain the range and velocity of the real targets. Experiments on simulated and measured datasets show that the proposed method has better antijamming detection performance than the traditional method, and does not need to estimate the jamming parameters.

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Section: Introductionmentioning

confidence: 99%

Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

Quan

Sha³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The synthetic aperture radar (SAR) has been widely applied in different fields as a result of its unique advantages, including its high resolution, wide swath, and all-time and all-weather application. However, SAR imagery has speckle noise interference and a unique imaging mechanism, which makes target classification in SAR images challenging (Gommenginger et al, 2019;Wang et al, 2020b). Ocean SAR scene classification is particularly difficult due to the following four issues:…”

Section: Introductionmentioning

confidence: 99%

KECANet: A novel convolutional kernel network for ocean SAR scene classification with limited labeled data

Bai

Zhang

et al. 2022

Front. Mar. Sci.

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The ability of synthetic aperture radar (SAR) to capture maritime phenomena is widely acknowledged. However, ocean SAR scene automatic classification remains challenging due to speckle noise interference, the nonlinearities and poor distinguishability of different geophysical phenomena. Kernel entropy component analysis (KECA) was recently proposed as a feature extraction approach. It is capable of handling nonlinear data and revealing different structures of interest. However, KECA suffers from high computational complexity, meaning it cannot penetrate deep for finer feature extraction. To address this issue, this paper proposes an efficient multilayer convolutional kernel network (denoted as KECANet) equipped with KECA for ocean SAR scene classification. The pivoted Cholesky decomposition is employed to accelerate KECA filtering in the network. KECA was trained on hand-labeled but limited samples describing ten oceanic or atmospheric phenomena. Several conventional and state-of-the-art deep learning methods were also included for comparison. According to the classification experiments, KECANet can greatly improve the classification precision of geophysical phenomena, considering that the precision, recall and F-score values increased by 13.3%, 2.3% and 12.2% in average. Overall, the results suggest that KECA is a promising approach for various applications in remote sensing image recognition.

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“…However, the real targets in the same direction as jammer are also suppressed in the cancellation process. In [15] and [16], the author recognized the deceptive jamming based on the geometric characteristics of the multistatic SAR and located the position of the jammer through the position relationship between the radar and the false target, so as to realize the anti-range deceptive jamming. In brief, 1D deceptive jamming of a single style can be easily identified and eliminated.…”

Section: і Introductionmentioning

confidence: 99%

An Anti-2D Deceptive Jamming Method for Multi-Baseline Interferometric SAR Based on Co-Localization Jammer

Lang

Tang

Mei

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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─Aimed at the problem that range-azimuth joint jamming can effectively generate high fidelity false targets in synthetic aperture radar (SAR) images, an anti-2D deceptive jamming method for multi-baseline interferometric SAR is proposed in this paper. The method consists of three steps: identification, classification and suppression. Firstly, the target is detected in the sea clutter background using constant false alarm rate algorithm, and then the false target is identified base on the difference of the spatial position of the target in different SAR images. Secondly, through the analysis of the generating principle of deceptive jamming, the false target is classified by multibaseline interferometric SAR according to partial prior knowledge, which is designed based on the differences between the interferometric phase of the true and false target. Finally, the position of the active jammer is co-located underlying the phase equalization characteristics between multiple baselines and the geometric configuration of the SAR, and the adaptive beamforming method is supplied to suppress the jamming signal. The proposed method has a significantly improved ability to suppress jamming relative to interferometric phase cancellation. The effectiveness of the proposed method is verified by simulation.

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An Antideceptive Jamming Method for Multistatic Synthetic Aperture Radar Based on Collaborative Localization and Spatial Suppression

Cited by 19 publications

References 45 publications

Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

KECANet: A novel convolutional kernel network for ocean SAR scene classification with limited labeled data

An Anti-2D Deceptive Jamming Method for Multi-Baseline Interferometric SAR Based on Co-Localization Jammer

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