Specific Emitter Identification Using Radio Environment Map Reconstruction

WANG Xuegang; WANG Fanggang; WANG Yizhuo

doi:10.11999/JEIT240050

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WANG Xuegang, WANG Fanggang, WANG Yizhuo. Specific Emitter Identification Using Radio Environment Map Reconstruction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240050

Citation:

WANG Xuegang, WANG Fanggang, WANG Yizhuo. Specific Emitter Identification Using Radio Environment Map Reconstruction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240050

WANG Xuegang, WANG Fanggang, WANG Yizhuo. Specific Emitter Identification Using Radio Environment Map Reconstruction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240050

Citation:

WANG Xuegang, WANG Fanggang, WANG Yizhuo. Specific Emitter Identification Using Radio Environment Map Reconstruction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240050

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Specific Emitter Identification Using Radio Environment Map Reconstruction

doi: 10.11999/JEIT240050

School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds: The Fundamental Research Funds for the Central Universities (2022JBQY004), The National Key R&D Program of China (2020YFB1806903), The National Natural Science Foundation of China (62221001), The Joint Funds for Railway Fundamental Research of National Natural Science Foundation of China (U2368201)

Received Date: 2024-01-24
Rev Recd Date: 2024-07-16

Available Online: 2024-07-24

Abstract

Abstract

The Radio Environment Map (REM) is one of the effective ways to represent the electromagnetic situation. Considering the issue that the actual observed incomplete spectrum map is corrupted by the impulses and the noises, the incomplete radio environment map is reconstructed and the specific emitter identification is performed based on the reconstructed maps. First, the spectrum map in the complex electromagnetic environment is modeled as the high-dimensional spectrum tensor, and the incomplete spectrum tensor is initially completed by the linear interpolation in preprocessing. Then, the vision transformer model is employed to solve the semantic segmentation problem in order to identify the spectrum semantic regions, in which the power of only one emitter dominates and the low-rank property of each semantic tensor is further preserved. To reconstruct the REM, the compressed tensor decomposition algorithm is proposed, and the expected signal spectrum and impulses are recovered utilizing the Alternating Direction Method of Multipliers (ADMM) in the semantic regions. Finally, the locations of the unknown emitters are detected on the reconstructed spectrum map. The proposed approach leverages the low-rank property of spectrum data and works well in wide-area electromagnetic scenarios involving multiple emitters. The simulation results demonstrate that the proposed approach outperforms the comparative approach in terms of reconstruction performance. It requires fewer observation samples to achieve the same spectrum map recovery accuracy and can accurately detect emitters.
- Specific emitter identification,
- Radio Environment Map (REM),
- Semantic segmentation,
- Tensor decomposition

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References(20)

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