基于頻譜地圖重構(gòu)的輻射源識(shí)別
doi: 10.11999/JEIT240050
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北京交通大學(xué)電子信息工程學(xué)院 北京 100044
基金項(xiàng)目: 中央高?；究蒲袠I(yè)務(wù)費(fèi)(2022JBQY004)，國(guó)家重點(diǎn)研發(fā)計(jì)劃(2020YFB1806903)，國(guó)家自然科學(xué)基金(62221001)，國(guó)家自然科學(xué)基金鐵路基礎(chǔ)研究聯(lián)合基金(U2368201)
Specific Emitter Identification Based on Radio Environment Map Reconstruction
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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)
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摘要: 無線環(huán)境地圖(REM)是呈現(xiàn)電磁態(tài)勢(shì)的一種有效形式，考慮實(shí)際觀測(cè)的不完整頻譜地圖受到干擾和噪聲污染的問題，該文對(duì)頻譜地圖進(jìn)行重構(gòu)，并在此基礎(chǔ)上完成輻射源識(shí)別。首先，將復(fù)雜電磁環(huán)境下的頻譜地圖建模為高維張量，在預(yù)處理中通過線性插值對(duì)其初始化補(bǔ)全。然后，使用視覺Transformer模型解決語(yǔ)義分割問題以識(shí)別頻譜語(yǔ)義區(qū)域，區(qū)域中僅單一輻射源功率占主導(dǎo)，每個(gè)語(yǔ)義張量的低秩性得以保留。提出了一種壓縮式張量分解算法，并采用交替方向乘子法(ADMM)在語(yǔ)義區(qū)域中重構(gòu)期望信號(hào)頻譜和干擾；最后，在重構(gòu)的頻譜地圖上檢測(cè)未知輻射源的位置。該方法能夠充分利用頻譜數(shù)據(jù)的低秩性，適用于廣域多輻射源個(gè)體的電磁場(chǎng)景。實(shí)驗(yàn)結(jié)果表明，所提方法比現(xiàn)有方法具有更優(yōu)的重構(gòu)性能，降低了達(dá)到相同頻譜地圖恢復(fù)精度時(shí)對(duì)觀測(cè)樣本比例的要求，并能夠準(zhǔn)確檢測(cè)輻射源。
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關(guān)鍵詞:
- 輻射源識(shí)別 /
- 頻譜地圖 /
- 語(yǔ)義分割 /
- 張量分解
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. -
1 基于語(yǔ)義分割的頻譜地圖重構(gòu)算法
輸入：初始補(bǔ)全張量$ {{\tilde{ {\boldsymbol{\mathcal{Y}}}}}_m} $，語(yǔ)義標(biāo)簽$ {{{\boldsymbol{\mathcal{L}}}}_m} $, $ m \in {{\mathcal{I}}_M} $，迭代次數(shù)K； 輸出：重構(gòu)的期望頻譜$ {\tilde {\boldsymbol{\mathcal{X}}}} $； 初始化：$ {\tilde{ {\boldsymbol{\mathcal{X}}}}}_m^{(1)} = {\bf{0}} $, $ {\tilde {{\boldsymbol{\mathcal{S}}}}}_m^{(1)} = 0 $, $ {\lambda ^{(1)}} = 0 $, $ {\beta ^{(1)}} = {10^{ - 6}} $,
$ {\beta _{\max }} = {10^{10}} $, $ \rho = 1.2 $，m = 1, $ k = 1 $；(1) 當(dāng)$ ||{{\boldsymbol{\mathcal{X}}}_m}||_{{\mathrm{F}}} ^2 $未收斂且$ k < K $，重復(fù)步驟(2)～(7)； (2) 使用式(22)更新$ {\boldsymbol{\mathcal{X}}}_m^{(k + 1)} $； (3) 使用式(24)更新$ {{\boldsymbol{\mathcal{S}}}}_m^{(k + 1)} $； (4) 使用式(25)更新$ \lambda _m^{(k + 1)} $； (5) 使用式(26)更新$ c_m^{(k + 1)} $； (6) $ {\beta ^{(k + 1)}} = \min \{ \rho {\beta ^{(k)}},{\beta _{{\text{max}}}}\} $； (7) k = k+1； (8) m = m+1； (9) 重復(fù)步驟(1)、步驟(8)，直到m = M； (10) $ {\tilde {\boldsymbol{\mathcal{X}}}}{\text{ = }}\displaystyle\sum\nolimits_{m = 1}^M {{{\boldsymbol{\mathcal{X}}}_m} \odot } {{{\boldsymbol{\mathcal{L}}}}_m} $。 
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