An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition

Liu, Zhidong; Zhang, Qun; Li, Kaiming

doi:10.3390/s21227702

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Whether it is a strip-covered target or a range of dense false realistic targets, it is obvious for the discriminator to distinguish interference by jamming signal. Moreover, the transmitting signal's parameters can be changed, or anti-jamming techniques can be used to filter out the jamming signal [19][20][21]. In [19], an anti-jamming method of double emission wave is proposed, which can filter out false targets along the range and azimuth dimensions, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the transmitting signal's parameters can be changed, or anti-jamming techniques can be used to filter out the jamming signal [19][20][21]. In [19], an anti-jamming method of double emission wave is proposed, which can filter out false targets along the range and azimuth dimensions, respectively. In [20], an adaptive anti-jamming method based on CS is proposed, which filters the ISRJ signal according to the jamming signal characteristics of energy concentrated and sparse.…”

Section: Introductionmentioning

confidence: 99%

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An ISAR Shape Deception Jamming Method Based on Template Multiplication and Time Delay

et al. 2023

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The deception jamming method based on Digital Radio Frequency Memory (DRFM) for Inverse Synthetic Aperture Radar (ISAR) has been a widely studied topic in recent decades. Typically, jamming signals generated using two-dimensional or three-dimensional false target models can create realistic false targets on the ISAR image. However, existing deception jamming methods cannot eliminate or revise the original echo, which can be retained by multiple anti-jamming methods once the radar judges out receiving the jamming signal. Additionally, these methods require large storage space for the models. Otherwise, the false targets cannot be generated realistically. To address these issues, this paper proposes a jamming signal generation algorithm based on two-dimensional template multiplication modulation and template time delay. The frequency shift and time delay relationship between the signals intercepted by the jammer and the real target echo is analyzed and derived in detail. With the use of these detailed derivations, it is possible to add and remove scatters by precisely locating the false scatter on the real ISAR image. The real target’s shape naturally changes as a result of the addition and removal of scatters. Furthermore, this method can adaptively change the resolution of the false target’s ISAR image with the radar pulse width and the accumulated pulse number. Meanwhile, the false target size on the ISAR image can be adjusted adaptively by altering the false template resolution. These features of the proposed method offer increased flexibility and efficiency for deception jamming. By accurately determining the position of false scatter on the ISAR image, this method offers improved performance compared with the existing techniques. Simulation results demonstrate the effectiveness of the proposed deception jamming method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An ISAR Shape Deception Jamming Method Based on Template Multiplication and Time Delay

et al. 2023

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“…In this case, the node will keep sensing to find an available channel until its energy depletes or eliminates the jammer from the networks. On the other side, when a node receives collected data from its neighbor during the jammer transmission will be considered to receive a large amount of noise [16,17].…”

Section: Introductionmentioning

confidence: 99%

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Aldosari¹

2023

Computers, Materials &Amp; Continua

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This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks on Wireless Sensor Networks (WSNs). Jamming is a type of Denial of Service (DoS) attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly. To defend and prevent such attacks, the first step is to detect them. The current detection approaches use centralized techniques to detect jamming, where each node collects information and forwards it to the base station. As a result, overhead and communication costs increased. In this work, we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer. As a result, we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks. Furthermore, we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB. With a detection accuracy of 99.7% for the k-nearest neighbors (KNN) algorithm and average testing accuracy of 99.9%, the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

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“…Several anti-jamming methods have been proposed, such as the RFI suppression method [ 12 ], WBI suppression method [ 13 ], STAP method [ 14 ], spatial location feature recognition method [ 15 ], and other methods [ 16 , 17 , 18 , 19 ]. Evaluation methods for the anti-jamming performance of SAR have also been studied [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Interferometric Phase Estimation in Dual-Channel Cancellation

Huang

Liu

Huang

et al. 2022

Sensors

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Multichannel SAR systems have grown rapidly over the past decade due to their powerful high-resolution and wide-swath (HRWS) capabilities. Because spatially separated channels also have the potential to suppress jamming, dual-channel cancellation is a general method that is effective regardless of the type of jamming signal. In this paper, the principle of dual-channel cancellation (DCC) is introduced, and several practical problems using DCC are also discussed. Moreover, this paper emphasizes interferometric phase estimation, which is the key to DCC. If the jamming-to-signal ratio (JSR) is high, the interferometric phase can be estimated accurately from the interferometry of two channel signals, but estimation becomes rather difficult when the JSR decreases. To solve the problem of interferometric phase estimation under a low JSR, a novel interferometric phase estimation method using cosine similarity is proposed in this paper. L-band airborne dual-channel SAR is performed to investigate the applicability of the method. The results not only prove that cosine similarity is an effective method for interferometric phase estimation, but also demonstrate the potential of DCC in the SAR anti-jamming processing.

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An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition

Cited by 5 publications

References 24 publications

An ISAR Shape Deception Jamming Method Based on Template Multiplication and Time Delay

An ISAR Shape Deception Jamming Method Based on Template Multiplication and Time Delay

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

A Novel Method for Interferometric Phase Estimation in Dual-Channel Cancellation

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