Specific Emitter Identification Using Radio Environment Map Reconstruction
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摘要: 无线环境地图(REM)是呈现电磁态势的一种有效形式，考虑实际观测的不完整频谱地图受到干扰和噪声污染的问题，该文对频谱地图进行重构，并在此基础上完成辐射源识别。首先，将复杂电磁环境下的频谱地图建模为高维张量，在预处理中通过线性插值对其初始化补全。然后，使用视觉Transformer模型解决语义分割问题以识别频谱语义区域，区域中仅单一辐射源功率占主导，每个语义张量的低秩性得以保留。提出了一种压缩式张量分解算法，并采用交替方向乘子法(ADMM)在语义区域中重构期望信号频谱和干扰；最后，在重构的频谱地图上检测未知辐射源的位置。该方法能够充分利用频谱数据的低秩性，适用于广域多辐射源个体的电磁场景。实验结果表明，所提方法比现有方法具有更优的重构性能，降低了达到相同频谱地图恢复精度时对观测样本比例的要求，并能够准确检测辐射源。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.
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1 基于语义分割的频谱地图重构算法
输入：初始补全张量$ {{\tilde{ {\boldsymbol{\mathcal{Y}}}}}_m} $，语义标签$ {{{\boldsymbol{\mathcal{L}}}}_m} $, $ m \in {{\mathcal{I}}_M} $，迭代次数K； 输出：重构的期望频谱$ {\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 $；(1) 当$ ||{{\boldsymbol{\mathcal{X}}}_m}||_{F} ^2 $未收敛且$ k < K $，重复步骤(2)～(7)； (2 使用式(22)更新$ {\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) 重复步骤(1)、(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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