Mainlobe Anti-Jamming via Eigen-Projection Processing and Covariance Matrix Reconstruction

Luo, Zhangkai; Wang, Huali; Lv, Wanghan; Tian, Hui

doi:10.1587/transfun.e100.a.1055

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…To analyze the experimental results, we conducted 200 Monte Carlo simulations. The proposed method is compared to SMI, EMP-SC [ 14 ], EMP-CMR [ 2 ], EMP-CMYR [ 17 ], EMP-CMIR [ 16 ], EMP-IAA [ 19 ], and EMP-CS [ 20 ]. It is worth noting that the received data contains no desired signal in EMP-SC and EMP-CMR.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, all the above three methods require that there is no desired signal in the received data. The Capon spatial–spectral estimation is used in [ 16 ] to reconstruct the interference plus noise covariance matrix (INCM). However, the eigen-oblique projection changes the eigenvector of sidelobe interference so that the processed sidelobe interference cannot be suppressed by the adaptive weight vector.…”

Section: Introductionmentioning

confidence: 99%

“…However, the eigen-oblique projection changes the eigenvector of sidelobe interference so that the processed sidelobe interference cannot be suppressed by the adaptive weight vector. Reference [ 17 ] corrects the sidelobe interference plus noise covariance matrix (SINCM) based on [ 16 ], but in the process of correction, the noise is reconstructed into color noise, which reduces the suppression performance of sidelobe interference. Reference [ 18 ] reduced the loss of the desired signal by eigen-oblique projection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple Mainlobe Interferences Suppression Based on Eigen-Subspace and Eigen-Oblique Projection

Ji¹,

Lu²,

Shan³

et al. 2022

Sensors

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When the desired signal and multiple mainlobe interferences coexist in the received data, the performance of the current mainlobe interference suppression algorithms is severely challenged. This paper proposes a multiple mainlobe interference suppression method based on eigen-subspace and eigen-oblique projection to solve this problem. First, use the spatial spectrum algorithm to calculate interference power and direction. Next, reconstruct the eigen-subspace to accurately calculate the interference eigenvector, then generate the eigen-oblique projection matrix to suppress mainlobe interference and output the desired signal without distortion. Finally, the adaptive weight vector is calculated to suppress sidelobe interference. Through the above steps, the proposed method solves the problem that the mainlobe interference eigenvector is difficult to select, caused by the desired signal and the mismatch of the mainlobe interference steering vector and its eigenvector. The simulation result proves that our method could suppress interference more successfully than the former methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Mainlobe Interferences Suppression Based on Eigen-Subspace and Eigen-Oblique Projection

Ji¹,

Lu²,

Shan³

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…At present, there are five main mature techniques for main-lobe jamming suppression, e.g., ADBF based on Blocking Matrix Preprocessing (BMP) [12][13][14][15] and Eigen-projection Matrix Preprocessing (EMP) [16][17][18], which can realize adaptive jamming cancellation from spatial filtering. However, the BMP-based method relies on the accurate information of the jamming angle, and the EMP-based method has the problem that the eigenvector corresponding to the main-lobe jamming is difficult to determine.…”

Section: Introductionmentioning

confidence: 99%

Monopulse Radar Target Detection in the Case of Main-Lobe Cover Jamming

2022

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Radar is known as the “eye” of modern warfare and plays a pivotal role in warfare, but the detection performance of radar systems is seriously affected by main-lobe cover jamming. To solve this problem, a radar target detection method based on four-channel monopulse radar is proposed in this paper. This method designed a spatial filter for main-lobe jamming cancellation, which could cancel the main-lobe jamming while keeping the target signal power unchanged, ensuring that the Electronic counter-countermeasure Improvement Factors (EIF) of the filtered signal always reach the maximum value to improve the target detection performance of monopulse radar. The Monte Carlo simulation results showed that the algorithm in this paper outperformed the conventional Main-Lobe Cancellation (MLC) algorithm in terms of EIFs and detection performance.

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“…Main lobe jamming is one of the most challenging jamming types because the jammer and the target are close enough that both are in the main beam of the radar. Common strategies to combat main lobe jamming involve identifying and eliminating jamming signals after the radar is jammed [1][2][3], which can be regarded as passive suppression methods. However, these methods usually require the jammer and the direction-of-look to be separable in angular space.…”

Section: Introductionmentioning

confidence: 99%

Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming

Jiu

Liu

et al. 2021

Remote Sensing

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To combat main lobe jamming, preventive measures can be applied to radar in advance based on the concept of active antagonism, and efficient antijamming strategies can be designed through reinforcement learning. However, uncertainties in the radar and the jammer, which will result in a mismatch between the test and training environments, are not considered. Therefore, a robust antijamming strategy design method is proposed in this paper, in which frequency-agile radar and a main lobe jammer are considered. This problem is first formulated under the framework of Wasserstein robust reinforcement learning. Then, the method of imitation learning-based jamming strategy parameterization is presented to express the given jamming strategy mathematically. To reduce the number of parameters that require optimization, a perturbation method inspired by NoisyNet is also proposed. Finally, robust antijamming strategies are designed by incorporating jamming strategy parameterization and jamming strategy perturbation into Wasserstein robust reinforcement learning. The simulation results show that the robust antijamming strategy leads to improved radar performance compared with the nonrobust antijamming strategy when uncertainties exist in the radar and the jammer.

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Mainlobe Anti-Jamming via Eigen-Projection Processing and Covariance Matrix Reconstruction

Cited by 13 publications

References 9 publications

Multiple Mainlobe Interferences Suppression Based on Eigen-Subspace and Eigen-Oblique Projection

Multiple Mainlobe Interferences Suppression Based on Eigen-Subspace and Eigen-Oblique Projection

Monopulse Radar Target Detection in the Case of Main-Lobe Cover Jamming

Robust Antijamming Strategy Design for Frequency-Agile Radar against Main Lobe Jamming

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