Specific Emitter Identification Based on Radio Environment Map Reconstruction

WANG Xuegang; WANG Fanggang; WANG Yizhuo

doi:10.11999/JEIT240050

Volume 46 Issue 10

Oct. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(10): 3949-3956

WANG Xuegang, WANG Fanggang, WANG Yizhuo. Specific Emitter Identification Based on Radio Environment Map Reconstruction[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3949-3956. doi: 10.11999/JEIT240050

Citation:

WANG Xuegang, WANG Fanggang, WANG Yizhuo. Specific Emitter Identification Based on Radio Environment Map Reconstruction[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3949-3956. doi: 10.11999/JEIT240050

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Specific Emitter Identification Based on 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

Publish Date: 2024-10-30

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)

References

[1]	HO K C and LE T K. Integrating AOA with TDOA for joint source and sensor localization[J]. IEEE Transactions on Signal Processing, 2023, 71: 2087–2102. doi: 10.1109/TSP.2023.3280417.
[2]	LIU Yan, SHEN Yuan, and WIN M Z. Single-anchor localization and synchronization of full-duplex agents[J]. IEEE Transactions on Communications, 2019, 67(3): 2355–2367. doi: 10.1109/TCOMM.2018.2878843.
[3]	YILMAZ H B and TUGCU T. Location estimation-based radio environment map construction in fading channels[J]. Wireless Communications and Mobile Computing, 2015, 15(3): 561–570. doi: 10.1002/wcm.2367.
[4]	SHIN B, LEE J H, KIM C, et al. LTE RSSI based vehicular localization system in long tunnel environment[J]. IEEE Transactions on Industrial Informatics, 2023, 19(11): 11102–11114. doi: 10.1109/TII.2023.3240690.
[5]	CHAUDHARI S and CABRIC D. Cyclic weighted centroid algorithm for transmitter localization in the presence of interference[J]. IEEE Transactions on Cognitive Communications and Networking, 2016, 2(2): 162–177. doi: 10.1109/TCCN.2016.2586078.
[6]	张国勇, 王军, 陈霄南, 等. 基于空间稀疏采样的频谱态势生成: 模型与算法[J]. 中国科学: 信息科学, 2022, 52(11): 2011–2036. doi: 10.1360/SSI-2021-0382. ZHANG Guoyong, WANG Jun, CHEN Xiaonan, et al. Spectrum situation generation from sparse spatial sampling: Model and algorithm[J]. Scientia Sinica Informationis, 2022, 52(11): 2011–2036. doi: 10.1360/SSI-2021-0382.
[7]	DEBROY S, BHATTACHARJEE S, and CHATTERJEE M. Spectrum map and its application in resource management in cognitive radio networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2015, 1(4): 406–419. doi: 10.1109/TCCN.2016.2517001.
[8]	SATO K, SUTO K, INAGE K, et al. Space-frequency-interpolated radio map[J]. IEEE Transactions on Vehicular Technology, 2021, 70(1): 714–725. doi: 10.1109/TVT.2021.3049894.
[9]	ZHANG Yongliang and MA Lin. Radio map crowdsourcing update method using sparse representation and low rank matrix recovery for WLAN indoor positioning system[J]. IEEE Wireless Communications Letters, 2021, 10(6): 1188–1191. doi: 10.1109/LWC.2021.3061539.
[10]	CAI Jianfeng, CANDÈS E J, and SHEN Zuowei. A singular value thresholding algorithm for matrix completion[J]. SIAM Journal on Optimization, 2010, 20(4): 1956–1982. doi: 10.1137/080738970.
[11]	SIDIROPOULOS N D, DE LATHAUWER L, FU Xiao, et al. Tensor decomposition for signal processing and machine learning[J]. IEEE Transactions on Signal Processing, 2017, 65(13): 3551–3582. doi: 10.1109/TSP.2017.2690524.
[12]	TANG Mengyun, DING Guoru, WU Qihui, et al. A joint tensor completion and prediction scheme for multi-dimensional spectrum map construction[J]. IEEE Access, 2016, 4: 8044–8052. doi: 10.1109/ACCESS.2016.2627243.
[13]	LIN Yun, TU Ya, DOU Zheng, et al. Contour Stella image and deep learning for signal recognition in the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(1): 34–46. doi: 10.1109/TCCN.2020.3024610.
[14]	WANG Chenyue, WU Yuhang, ZHOU Fuhui, et al. Accurate spectrum map construction using an intelligent frequency-spatial reasoning approach[C]. 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022: 3460–3465. doi: 10.1109/GLOBECOM48099.2022.10001024.
[15]	SHRESTHA S, FU Xiao, and HONG Mingyi. Deep spectrum cartography: Completing radio map tensors using learned neural models[J]. IEEE Transactions on Signal Processing, 2022, 70: 1170–1184. doi: 10.1109/TSP.2022.3145190.
[16]	HU Tianyu, HUANG Yang, CHEN Junting, et al. 3D radio map reconstruction based on generative adversarial networks under constrained aircraft trajectories[J]. IEEE Transactions on Vehicular Technology, 2023, 72(6): 8250–8255. doi: 10.1109/TVT.2023.3239556.
[17]	LEVIE R, YAPAR Ç, KUTYNIOK G, et al. RadioUNet: Fast radio map estimation with convolutional neural networks[J]. IEEE Transactions on Wireless Communications, 2021, 20(6): 4001–4015. doi: 10.1109/TWC.2021.3054977.
[18]	LIN Yun, ZHAO Haojun, MA Xuefei, et al. Adversarial attacks in modulation recognition with convolutional neural networks[J]. IEEE Transactions on Reliability, 2021, 70(1): 389–401. doi: 10.1109/TR.2020.3032744.
[19]	MENG Lingchen, LI Hengduo, CHEN B C, et al. AdaViT: Adaptive vision transformers for efficient image recognition[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 12299–12308. doi: 10.1109/CVPR52688.2022.01199.
[20]	LI Hongyu, LI Jie, SHEN Feng, et al. SOC algorithm-based framework for spectrum occupancy completion[J]. IEEE Transactions on Cognitive Communications and Networking, 2023, 9(5): 1155–1166. doi: 10.1109/TCCN.2023.3269763.